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"chunk": "# Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots \n\nPublished as part of Chemical Reviews special issue “Soft Robotics”. Jiangtao Su, Ke He, Yanzhen Li, Jiaqi Tu, and Xiaodong Chen\\*",
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"id": 2,
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"chunk": "# Cite This: https://doi.org/10.1021/acs.chemrev.4c00906",
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"category": " References"
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"chunk": "# ACCESS \n\nMetrics & More \n\nArticle Recommendations \n\nABSTRACT: Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.",
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"category": " Abstract"
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"id": 4,
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"chunk": "# CONTENTS \n\n1. Introduction \n2. Sensing Technologies and Materials 2.1. Pressure Sensors 2.1.1. Piezoresistive 2.1.2. Capacitive 2.1.3. Piezoelectric 2.1.4. Triboelectric 2.1.5. Other (Magnetic and Optical) 2.2. Strain Sensors 2.2.1. Resistive 2.2.2. Capacitive 2.2.3. Piezoelectric and Triboelectric 2.2.4. Other 2.3. Temperature Sensors 2.3.1. Resistive type 2.3.2. Thermistor 2.3.3. Thermocouple 2.3.4. Other 2.4. Optical Sensors 2.4.1. Mechanism and Materials 2.4.2. Integrated Photonic Systems 2.5. Chemical Sensors 2.5.1. Carbon Materials \n\n \n\n2.5.2. Transition Metal Dichalcogenides \n(TMDs) Z \n2.5.3. Metal−Organic Frameworks (MOFs) Z \n2.5.4. Metal Oxides and Composites Z \n2.5.5. Other Materials Z \n2.6. Acoustic Sensors AB \n2.6.1. Ultrasound Waves AB \n2.6.2. Audible Waves AD \n2.7. Electromagnetic Sensors AE \n2.7.1. Magnetoreception AE \n2.7.2. Electroreception AE \n2.8. Multimodal Integration AG \n2.8.1. Normal and Shear Force AG \n2.8.2. Other Integrated Multimodal Sensing \nTechnologies AI \n2.9. Future Development AJ \n3. Actuation Modalities and Materials AL \n\n3.1. Fluidic Actuators 3.1.1. Hydraulic 3.1.2. Pneumatic 3.2. Electroactive Actuators 3.2.1. Dielectric Elastomer Actuators 3.2.2. Hydraulically Amplified Electrostatic Actuators 3.2.3. Piezoelectric Actuators 3.2.4. Electrochemical Actuators 3.3. Magnetic Actuators 3.3.1. Solid-state Magnetic Robots 3.3.2. Liquid-State Magnetic Robots 3.3.3. Magnetic Robot Swarm 3.4. Optical Actuators 3.4.1. Liquid-Crystal Polymers 3.4.2. Hydrogels 3.4.3. Shape-Memory Polymers 3.4.4. Light-Responsive Liquids 3.5. Thermal Actuators 3.5.1. Liquid-Crystal Elastomers 3.5.2. Shape-Memory Materials 3.6. Chemical Actuators 3.6.1. Organic Vapors and Solvents 3.6.2. Humidity-Related Reactions 3.6.3. Enzymes 3.6.4. pH 3.7. Other Actuation Modalities 3.7.1. Acoustic 3.7.2. Biohybrid 3.7.3. Humidity 3.7.4. Energy Storage 3.7.5. Phase Change 3.7.6. Combustion 3.8. Future Development \n4. Structure and Mechanics 4.1. Buckling Structures 4.2. Kirigami and Origami 4.3. Fibers and Fabrics 4.4. Other Structures \n5. Fabrication Techniques 5.1. Templating 5.1.1. Molding 5.1.2. Lithography 5.1.3. Coating 5.1.4. Printing 5.2. Laser-Assisted Fabrication 5.2.1. Cutting 5.2.2. Engraving 5.2.3. Surface Modification 5.3. 3D Printing 5.3.1. Principles of 3D Printing 5.3.2. Printing Soft Electronic Devices 5.3.3. Printing Soft Actuators 5.3.4. Printing Soft Robots with Sensing Abilities 5.4. Transfer Printing 5.4.1. Mechanically Guided 5.4.2. Stimuli-Triggered 5.4.3. Other Transfer Printings Methods 5.4.4. 3D Curvy Electronics via Transfer Printing 5.5. Assembly 5.5.1. Materials Level \n\n5.5.2. Electronic Device Level 5.5.3. Robotic System Level 6. Sensorimotor Control 6.1. Sensorimotor Control Frameworks 6.2. Control of Soft Robots 6.2.1. Model-Based Control 6.2.2. Data-Driven Control 6.2.3. Hierarchical/Hybrid Control 6.3. Emerging Approaches 6.3.1. Embodied Intelligence 6.3.2. Morphological Computation 6.3.3. Mechanical Computing 7. Artificial Intelligence (AI) in Soft Robots with Sensorimotor Functions 7.1. Machine Learning Framework 7.2. AI for Flexible Electronic Sensing Devices 7.3. AI for Soft Robotic Systems 8. Applications 8.1. Exploration 8.1.1. Aerial 8.1.2. Terrestrial 8.1.3. Aquatic 8.1.4. Cross-Media 8.2. Healthcare 8.2.1. Exoskeletons 8.2.2. Prosthetics 8.2.3. Artificial Organs 8.2.4. Drug Delivery 8.2.5. Catheters 8.2.6. Surgical Tools 8.3. Extended Reality (XR: AR/VR/MR) 8.3.1. Haptic Feedback Devices 8.3.2. XR Applications and Human−Machine Interaction 8.4. Manipulation 8.4.1. Object Handling 8.4.2. Object Recognition 9. Considerations for Future Development 9.1. Materials Discovery 9.2. Biomimicking 9.3. Energy 9.4. Manufacturing 9.5. Artificial Intelligence 9.6. Sustainability 10. Concluding Remarks Author Information Corresponding Author Authors Author Contributions Notes Biographies Acknowledgments References",
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"category": " Introduction"
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"chunk": "# 1. INTRODUCTION \n\nSensorimotor processes in biological species constitute an integrated system of sensory input and motor output essential for interaction with the environment.1−6 Sensory receptors, specialized for detecting various stimuli, such as light, sound, or touch, transmit this information to corresponding sensory cortexes in the central nervous system (CNS).7−16 The CNS processes these sensory inputs to generate appropriate motor responses, thereby establishing a continuous cycle of sensing, decision-making, and action that coordinates all the sensory organs, brain, and muscles for a specific task (Figure 1a). This intricate system facilitates both simple reflex actions and complex, coordinated behaviors, such as locomotion, flight, and predation in animals (Figure 1b).17−22 These adaptive, adept, agile, autonomous, and dexterous sensorimotor activities are a masterpiece for the seamless coordination of sensory receptors, controllers, and muscles or actuators. The integration of sensorimotor functions is vital for survival and daily activities, enabling organisms to respond adeptly to environmental changes, adapt through experience, and acquire new skills via feedback and practice.23,24 By contrast, the adaptability, agility, dexterity, and intelligence of conventional \n\n \nFigure 1. Sensorimotor systems in human beings, animals, and soft robot. (a) Schematic illustration of human sensorimotor system. This system is mainly composed of sensory system (vision, touch, hearing, smell, and taste), muscular system (a variety of muscles spread throughout the whole body), and peripheral nervous system, and central nervous system (five sensory cortexes corresponding to the sensory system and one motor cortex). These systems coordinate synergically for a specific task. (b) Examples of sensorimotor actions in animals, such as elephant trunk for eating, octopus (escaping from dangers), hawk (patrolling for prey), star-nosed mole (exploring surroundings), gecko (preying), and bee (flying). These adaptive, adept, agile, autonomous, and dexterous sensorimotor activities are a masterpiece for the seamless coordination of sensory receptors, controllers, and muscles or actuators. Reproduced with permission from ref 37. Copyright 2020 Elsevier. Reproduced with permission from ref 38. Copyright 2024 American Association for the Advancement of Science. Reproduced with permission from ref 39. Copyright 2023 Springer Nature. Reproduced with permission from ref 40. Copyright 2001 Springer Nature. Reproduced with permission from ref 41. Copyright 2023 Deutsche Gesellschaft für Herpetologie and Terrarienkunde (DGHT). Reproduced with permission from ref 42. Copyright 2024 Taylor & Francis. (c) A simplified architecture for sensory motor control system. In this system, physical and chemical stimuli from the environments are first transduced to electrical signals by sensory receptors (sensors), followed by transmission and processing of the data in central nervous system (controller). After multimodal date fusion and high-level computation and interpretation, movement commands are sent out from motor cortex in the central nervous system, and corresponding actions or muscle movements are made to accomplish a specific task. Sensory inputs and motor outputs are made continuously, so that a cycle of closed-loop sensory-motor coordination for daily activities is established. (d) Drawing analogy from biological species, similar sensorimotor control process can also be built in robots. Here we term this as sensorimotor robot, in which a wide spectrum of sensors, actuators, and controllers that mimic their counterparts in biological species are required. \n\n \nFigure 2. An overview of the development of soft sensors and actuators in last 70 years and their convergence. Silk screen plastic pressure arrays. Reproduced with permission from ref 44. Copyright 1954 Elsevier. Flexible piezo-resistive sensor. Reproduced with permission from ref 45. Copyright 1988 SPIE. Soft tribo-sensor. Reproduced with permission from ref 46. Copyright 1999 SAGE Publications. OFET pressure sensor matrix. Reproduced with permission from ref 47. Copyright 2004 United States National Academy of Sciences. Wearable glucose sensor. Reproduced with permission from ref 48. Copyright 2006 Elsevier. Stretchable silicon circuits. Reproduced with permission from ref 49. Copyright 2008 American Association for the Advancement of Science. Electronic eyes. Reproduced with permission from ref 50. Copyright 2008 Springer Nature. Nonvolatile memory transistors. Reproduced with permission from ref 51. Copyright 2009 American Association for the Advancement of Science. Skin-like pressure and strain sensors. Reproduced with permission from ref 52. Copyright 2011 Springer Nature. Epidermal electronics. \n\nReproduced with permission from ref 53. Copyright 2011 American Association for the Advancement of Science. Imperceptible electronics. Reproduced with permission from ref 64. Copyright 2013 Springer Nature. Integrated sweat sensor. Reproduced with permission from ref 65. Copyright 2016 Springer Nature. Intrinsically stretchable transistor. Reproduced with permission from ref 66. Copyright 2018 Springer Nature. Tactile glove. Reproduced with permission from ref 67. Copyright 2019 Springer Nature. Ultrasensitive strain gauges. Reproduced with permission from ref 68. Copyright 2020 Springer Nature. Acoustic fabrics. Reproduced with permission from ref 69. Copyright 2022 Springer Nature. Artificial eyes in harsh environment. Reproduced with permission from ref 70. Copyright 2023 American Association for the Advancement of Science. Bioresorbable ultrasound sensor. Reproduced with permission from ref 71. Copyright 2024 American Association for the Advancement of Science. Wearable exoskeleton. Reproduced with permission from ref 83. Copyright 1957 Cyberneticzoo. Pneumatic manipulator. Reproduced with permission from ref 117. Copyright 2021 American Association for the Advancement of Science. Soft pneumatic elephant trunk. Reproduced with permission from ref 84. Copyright 1984 Cyberneticzoo. Mechanochemical actuators. Reproduced with permission from ref 85. Copyright 1987 Taylor $\\&$ Francis. Shape memory alloy actuator. Reproduced with permission from ref 86. Copyright 1989 Taylor & Francis. Piezoelectric actuators. Reproduced with permission from ref 87. Copyright 1993 Cambridge University Press. Conjugated polymer microactuators. Reproduced with permission from ref 88. Copyright 2000 American Association for the Advancement of Science. Muscular films actuators. Reproduced with permission from ref 89. Copyright 2007 American Association for the Advancement of Science. Liquid-crystal network actuators. Reproduced with permission from ref 90. Copyright 2009 Springer Nature. Multigait soft robot. Reproduced with permission from ref 91. Copyright 2011 United States National Academy of Sciences. Octopus-inspired soft robot. Reproduced with permission from ref 92. Copyright 2012 Taylor & Francis. Soft robot powered by explosion. Reproduced with permission from ref 93. Copyright 2013 Wiley. Entirely soft autonomous robots. Reproduced with permission from ref 110. Copyright 2016 Springer Nature. Magnetic-driven soft robot. Reproduced with permission from ref 111. Copyright 2018 Springer Nature. Ultragentle manipulator. Reproduced with permission from ref 112. Copyright 2019 American Association for the Advancement of Science. Deep sea soft robot. Reproduced with permission from ref 113. Copyright 2021 Springer Nature. Surgical robots. Reproduced with permission from ref 114. Copyright 2023 American Association for the Advancement of Science. Magnetic continuum robot. Reproduced with permission from ref 115. Copyright 2024 American Association for the Advancement of Science. Soft prosthetic hand by optical waveguides. Reproduced with permission from ref 118. Copyright 2016 American Association for the Advancement of Science. Robot with stretchable electroluminescent skin. Reproduced with permission from ref 119. Copyright 2016 American Association for the Advancement of Science. Strain sensing actuator. Reproduced with permission from ref 120. Copyright 2016 Wiley. Optoelectronic sensory foams with proprioception. Reproduced with permission from ref 121. Copyright 2018 American Association for the Advancement of Science. Soft somatosensitive actuators. Reproduced with permission from ref 122. Copyright 2018 Wiley. OmniSkins. Reproduced with permission from ref 123. Copyright 2018 American Association for the Advancement of Science. Actuators with embedded flex sensors. Reproduced with permission from ref 124. Copyright 2018 Elsevier. Wirelessly activated fully soft robots by e-skin. Reproduced with permission from ref 125. Copyright 2018 American Association for the Advancement of Science. Resistive sensors on soft robot. Reproduced with permission from ref 126. Copyright 2019 Frontiers. E-skin on soft robotic hand. Reproduced with permission from ref 127. Copyright 2019 Wiley. Earthworm-inspired soft robot with perceptive skin. Reproduced with permission from ref 128. Copyright 2019 IOP Publishing. Heterogeneous sensing in a multifunctional soft sensor. Reproduced with permission from ref 129. Copyright 2020 American Association for the Advancement of Science. TENG sensors on soft robot. Reproduced with permission from ref 130. Copyright 2020 Springer Nature. Actuators embedded with paper electronics. Reproduced with permission from ref 131. Copyright 2020 Wiley. Integration of sensing and shape-deforming capabilities for a bioinspired soft robot. Reproduced with permission from ref 132. Copyright 2021 Elsevier. Multifunctional e-skin on soft gripper. Reproduced with permission from ref 133. Copyright 2021 American Association for the Advancement of Science. Origami actuators with on-board sensing. Reproduced with permission from ref 134. Copyright 2021 Wiley. Proprioceptive soft robot module. Reproduced with permission from ref 135. Copyright 2022 MDPI. Proprioception and exteroception in soft robot. Reproduced with permission from ref 136. Copyright 2023 Wiley. Octopus-inspired sensorized soft arm. Reproduced with permission from ref 137. Copyright 2023 American Association for the Advancement of Science. Soft fluidic robots with sensing capabilities. Reproduced with permission from ref 138. Copyright 2024 Springer Nature. \n\nrobot pale when compared with that of biological species, due to a lack of multimodal, long-term, and synergistic sensorimotor process with continuous sensory input, motor output and learning. Nevertheless, this foundational concept in biology can be mirrored in the design of soft robotic systems, where environmental signals are detected by sensors, analyzed by a central processing unit (CPU), and followed by precise actuation (Figure 1c).25−36 \n\nHerein, we term this kind of intelligent machine as sensorimotor robots (Figure 1d). Thus, sensorimotor robots are engineered systems designed to emulate the sensorimotor processes found in biological organisms. These robots integrate sensory data acquisition with closed-loop motor control to interact dynamically, effectively, and continuously with their environment. Key components and functionalities of sensorimotor robots include: a) Sensors: The robot is equipped with various sensors with corresponding data acquisition and communication platforms to detect environmental stimuli and their own posture (exteroception and proprioception). These can include cameras (vision), microphones (sound), gas sensors (smell), touch sensors, gyroscopes (balance), and more. These sensors serve as bridges connecting the physical and digital worlds, providing quantitative and digitalized inputs for the robotic system. b) Processing units or controllers: The robot’s central processing unit (CPU) or artificial intelligence (AI) system processes the sensory data and makes decisions about the appropriate actions to take. While the former involves interpreting the sensory inputs to understand the environment and context, the latter one refers to algorithms and other approaches for path planning, obstacle avoidance, object recognition, and more. c) Actuators: The robot uses actuators, such as motors and servos, to execute the planned actions. These actuators are responsible for precise movements and manipulations required for the robot to perform its tasks. A wide range of soft actuation mechanisms, including fluidicdriven actuators, dielectric elastomer actuators, and stimuliresponsive smart materials, hold promise for the actuation of sensorimotor robots. By emulating the sensing−decision− action loop observed in biological entities, we aim to develop a robust sensorimotor framework for soft robots, enhancing their capability to interact with and adapt to their surroundings in a manner analogous to living organisms. \n\nIn this framework, sensing and actuation are two of the most critical foundations for soft robotics and Figure 2 listed the development of representative work about soft sensors and actuators and their intersections in the past 70 years. Sensing involves the detection of various environmental stimuli through specialized sensors, which can be traced back to thousands of years ago.43 Conventional sensors and devices for such detection are hard, rigid, and brittle, such as the ancient setup for earthquake detection and modern computer platform based on silicon chips, which is not compatible with soft systems. Owning to the breakthroughs made in soft materials, conductive polymers, nanomaterials, mechanics, and fabrication techniques, a variety of soft sensors are created, ranging from different types of pressure, chemical, optical sensors to memory devices and integrated flexible sensing devices.44−63 Recently, with the technological maturity, more and more attention about flexible devices has been paid to higher density and performance, harsh working conditions, multimodal sensing capabilities, sustainability, etc.64−82 On the other side, the research on soft actuators was stemmed from the implementation of wearable exoskeletons for the disabled.83 After that, interests about soft robots were increasing and various actuation mechanisms (e.g., pneumatic, chemical, piezoelectric, etc.) and smart materials (e.g., shape-memory materials, polymers, liquid-crystal materials, etc.) are proposed and discovered, achieving controllable movement of soft matter.84−103 However, compared with the soft creatures in nature, such robots can only achieve simple movement modes and have limited functionalities, which restricts their potentials as soft machines.104−109 Thus, in the past ten years, tremendous efforts in both materials and manufacturing techniques have been made to creating more advanced soft machines, enabling a number of unimaginable functionalities, such as autonomy, reconfigurability, working in extreme conditions, etc.110−115 \n\n \nFigure 3. An overview of sensorimotor materials for soft robots. The organization starts from materials and structures for sensors and actuators for soft robots, which are two fundamental physical foundation. Followed by discussion of manufacture techniques (such as templating, laser fabrication, 3D printing, transfer printing, etc.) and sensorimotor control (such as sensorimotor framework, control strategies, etc.), artificial intelligence used in soft electronics and robots are introduced. The synergistic work of these aspects from materials, structures, and manufacture to sensorimotor control and AI enables a variety of applications, such as exploration, healthcare, AR/VR, human−machine interaction, manipulation, and recognition. Reproduced with permission from ref 151. Copyright 2023 Springer Nature. Reproduced with permission from ref 152. Copyright 2018 Oxford University Press. Reproduced with permission from ref 153. Copyright 2022 Wiley. Reproduced with permission from ref 154. Copyright 2022 Wiley. Reproduced with permission from ref 155. Copyright 2014 Springer Nature. Reproduced with permission from ref 156. Copyright 2006 Springer Nature. Reproduced with permission from ref 157. Copyright 2024 Springer Nature. Reproduced with permission from ref 158. Copyright 2023 Springer Nature. Reproduced with permission from ref 159. Copyright 2019 Springer Nature. Reproduced with permission from ref 160. Copyright 2022 Springer Nature. Reproduced with permission from ref 163. Copyright 2023 Springer Nature. Reproduced with permission from ref 161. Copyright 2022 Springer Nature. Reproduced with permission from ref 164. Copyright 2023 American Association for the Advancement of Science. Reproduced with permission from ref 162. Copyright 2020 American Association for the Advancement of Science. \n\nUndoubtably, while soft machines are promising in applications ranging from prosthetic hands and exoskeletons to AR/VR and exploration due to their intrinsic softness and compliance, soft electronic devices are paving the way for their long-term development toward full autonomy and intelligenc e.116 However, most of reported soft robots can only move and respond passively, lacking the function of sensing internal and external information. This absence of sensory input could not make up a complete sensorimotor system and makes the soft machine impotent working in changing and unstructured environments. \n\nIn contrast, by integrating advanced sensing and actuation capabilities, soft robots can navigate and manipulate their environments in perceptive and active ways, due to the coordination of sensory inputs and motor outputs, mimicking the sophisticated sensorimotor loop of human beings.139,140 This is a critical step for the transition of soft robots from structure to function and further to intelligence. Nevertheless, compared with the vast number of works about soft sensors and actuators, the investigation to their intersection has a shorter history and there are limited number of representative works that achieved the integration of sensors on soft machines for stimuli and position detection, object recognition, and manipulation.118−138,141 As the advancements of science and technology, research on this sensorimotor intersection is sure to rise and it is worth to review the developments in sensorimotor of soft machines to shed light on its future. Although there are a number of papers have reviewed the progress in soft robots and flexible sensors, they are limited either in lacking of a comprehensive framework of sensorimotor (e.g., sensors, actuators, controls, etc.) or the completeness and depth of sensory and actuation system (e.g., materials, structures, manufacturing, etc.).142−150 Aiming to address these limitations and build a groundwork for the continued sensorimotor work on soft machines, here we provided a holistic review on the sensorimotor materials for soft robots. \n\n \nFigure 4. Chemicals of commonly used substrate materials and functional materials in flexible sensing devices and a summary of Young’s modulus and conductivity of these materials. Values were extracted from refs 183−200. \n\nIn this review, we extrapolated an exhaustive story about sensorimotor materials for soft machines from different perspectives that are indispensable for this interdisciplinary field, ranging from sensing/actuation materials, structures, and mechanics to manufacturing techniques, sensorimotor control, artificial intelligence, and applications (Figure 3).151−164 First of all, while sensing and actuation are two of the most important foundations involved in the architecture of sensorimotor, starting from materials aspects, we made a complete review with regards to various types of flexible sensors and soft actuators in terms of their working principles, materials composition, and device performance and advancements. \n\nWith equal significance, structures and mechanics provide another design dimension and guideline for flexible electronic devices and soft machines. We systematically reviewed the commonly used structures (such as buckling structure, kirigami, origami, fibers, fabrics, etc.) involved in electronic devices and soft robots. Then, five mostly employed fabrication techniques in soft electronics and actuators (templating, laser assisted fabrication, 3D printing, transferring printing and assembly) are discussed in terms of their working principles and corresponding fabrication examples. Drawing the analogy from sensorimotor loop or sensorimotor control from biology, we next built a comprehensive framework of sensorimotor control for soft robots that links sensing−decision−action in a closed-loop network and explored the strategies for soft robotic control. Afterward, AI involved in flexible sensing devices and soft robotic system is discussed. Followed by this, examples about the application of soft robots in a variety of scenarios are offered, ranging from exploration and AR/VR to healthcare and manipulation. Last but not least, insights with regards to the future development of soft machines are provided, aiming at shedding light on the long-standing issues and challenges faced by soft robotic research and human beings. \n\n \nFigure 5. Various mechanisms of pressure sensors. $\\left(\\mathsf{a{-}d}\\right)$ Piezoresistive pressure sensors based on geometrical effect (a), disconnection mechanism (b), crack propagation (c), and tunnelling effect (d). Reproduced with permission from ref 201. Copyright 2009 Wiley. Reproduced with permission from ref 202. Copyright 2014 Wiley. Reproduced with permission from ref 203. Copyright 2014 Springer Nature. Reproduced with permission from ref 204. Copyright 2020 Springer Nature. $\\left(\\mathrm{e-g}\\right)$ capacitive pressure sensors based on electrodes distance change (e), relative electrode areas change (f), and dielectric change $(\\mathbf{g})$ . Reproduced with permission from ref 205. Copyright 2016 Wiley. Reproduced with permission from ref 206. Copyright 2020 American Chemical Society. Reproduced with permission from ref 207. Copyright 2021 Wiley. $(\\mathrm{h-k})$ Triboelectric pressure sensors based on vertical contact mode $\\mathrm{(h)}$ , single electrode mode (i), sliding mode (j), and freestanding mode (k). Reproduced with permission from ref 208. Copyright 2012 American Chemical Society. Reproduced with permission from ref 209. Copyright 2014 American Chemical Society. Reproduced with permission from ref 210. Copyright 2014 Wiley. Reproduced with permission from ref 211. Copyright 2014 Wiley. $\\left(\\operatorname{l-n}\\right)$ Other mechanisms for pressure sensors, including piezoelectric pressure sensors (l), optical-based pressure sensors $\\mathrm{(m)}$ , and magnetic pressure sensors $\\mathbf{\\rho}(\\mathbf{n})$ . Reproduced with permission from ref 212. Copyright 2015 Springer Nature. Reproduced with permission from ref 118. Copyright 2016 American Association for the Advancement of Science. Reproduced with permission from ref 213. Copyright 2024 MDPI, Basel, Switzerland.",
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"category": " Introduction"
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"chunk": "# 2. SENSING TECHNOLOGIES AND MATERIALS \n\nSensing technologies have revolutionized the way we interact with the world, playing a crucial role in a wide range of applications, from healthcare monitoring and environmental sensing to industrial automation and wearable electronic s.165−172 Flexible sensors demonstrate significant advantages over conventional rigid sensors in terms of adaptability, comfort, and integration with dynamic and irregular surfaces.173,174 The effectiveness and versatility of these technologies are deeply rooted in the materials and mechanisms that underpin their functionality. The rapid development of nanomaterials, composites, carbon-based materials, conductive polymers, organic semiconductors, liquid metal, and ionic elastomers has spurred the emergence of a wide array of flexible devices and platforms.175−182 These materials offer exceptional electrical properties and mechanical flexibility, enabling the creation of devices that can bend and stretch without losing functionality. \n\nAs sensing technologies continue to evolve, ongoing research and development of new materials will undoubtedly lead to even more innovative and impactful applications. This section provides a brief overview of the various sensing technologies and materials. In terms of materials, we categorize them into substrate materials and functional materials and Figure 4 listed the chemical structures of most widely used materials as well as their Young’s modulus and conductivity, which are two key indicators for the mechanical and electrical properties of soft electronic devices. While soft materials play indispensable roles in the sensing and actuation of soft robots, the definition of softness is very diverse. To be more specific, softness in materials is a key characteristic defined by their ability to deform under applied forces, and it is typically quantified by properties such as Young’s modulus, shear modulus, compressive modulus, indentation hardness, strainto-failure and others. Young’s modulus, which represents the ratio of stress to strain in the elastic region, is particularly important in assessing the stiffness of a material, with lower values indicating greater softness, as these materials deform more easily under the same stress. Thus, Young’s modulus is considered as one crucial indicator of the softness of materials employed in flexible sensors and soft actuators. It can be shown from Figure 4, the lower the Young’s modulus, the softer the materials are. Similarly, materials with lower shear modulus and compressive modulus are also considered softer, as they exhibit greater deformation when subjected to shear or compressive forces. Softness can also be assessed through methods such as indentation hardness, where the depth of the indentation made by a probe under a specified load is measured, with softer materials showing deeper impressions. Strain-to-failure, which measures the extent of deformation before a material breaks, is another important metric, with softer materials typically exhibiting larger strains before rupture. Additionally, Poisson’s ratio, which quantifies the lateral strain relative to axial strain when a material is stretched or compressed, tends to be higher in softer materials, indicating that they deform more easily in multiple directions. In soft robotics, these properties are crucial as they directly influence the material’s performance in dynamic environments. Soft materials, particularly those with low Young’s modulus and high strain-to-failure, are selected to provide flexibility, compliance, and adaptability, enabling soft robots to interact safely with delicate objects or human skin. Such materials are essential for applications that require high degrees of flexibility and safe human−robot interaction, as well as for creating soft actuators and grippers that can manipulate objects gently without causing damage. \n\nBeginning with mechanical sensors, including pressure sensors and strain sensors, different mechanisms are reviewed. These mechanisms include piezoresistive, capacitive, piezoelectric, and triboelectric, along with other methods such as magnetic and optical sensing. Each technology offers unique advantages and challenges, making them suitable for different applications and environments. Following this, temperature sensing technologies are examined, with a focus on resistive sensors, thermistors, and thermocouples. These sensors are essential for many applications, from industrial control to medical diagnostics. Photonic sensing is then discussed, primarily focusing on the interaction of light with materials to detect environmental changes, offering high sensitivity and specificity. Chemical sensing technologies are presented next, with a focus on materials such as carbon-based materials, transition metal dichalcogenides (TMDs), metal−organic frameworks (MOFs), metal oxides, and their composites. These materials are crucial for detecting chemical substances in fields like environmental monitoring, healthcare, and safety. Acoustic sensing technologies, covering both ultrasound and audible waves, are also important for applications ranging from medical imaging to structural health monitoring. Electromagnetic sensing, including magnetic and electric sensors, detect changes in electromagnetic fields and are crucial for applications in communications, navigation, and security. Finally, this section explores multimodal integration in sensing technologies, focusing on the integration of normal and shear forces and other advanced multimodal sensing technologies. This integration enhances the functionality and performance of sensors, enabling them to provide more comprehensive and accurate data.",
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"category": " Introduction"
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"chunk": "# 2.1. Pressure Sensors \n\nIndeed, tactile sensors utilize various mechanisms to detect and respond to external stimuli. Among the most common are resistance, capacitance, triboelectricity, and others, each employing unique materials and structural designs. We systematically summarized the working mechanisms of these sensors in Figure 5. \n\nResistance-based tactile sensors utilize various mechanisms to detect and respond to pressure or force. One common method involves the geometric changes of conductive materials or polymers, where the resistance changes with deformation (Figure 5a).201 Another mechanism is based on the disconnection mechanism, where the conductive path within the materials is disrupted or reconnected under external stimuli, such as pressure. This connection typically occurs between electrodes and piezoresistive materials (Figure 5b).202 Additionally, some sensors operate on the principle of crack propagation, where external force generates cracks in a conductive layer, leading to an increase in resistance. Upon release of the force, the conductive path reconnects, resulting in the recovery of resistance (Figure 5c).203 Another mechanism is based on the tunneling effect, which relies on specific material properties to detect changes in resistance (Figure 5d).204 \n\nCapacitance-based sensors, on the other hand, measure changes in capacitance. Based on the calculation equation of capacitance, there are three main factors that influence the capacitance change, including the relative area between electrodes, the dielectric constant of the dielectric material, and the distance between electrodes. One design involves monitoring changes in the distance between the electrodes (Figure 5e).205 When the dielectric material is deformable, external stimuli cause changes in the distance between electrodes, resulting in a change in capacitance. Another approach is to detect changes in the relative electrode area (Figure 5f).206 External stimuli induce displacement of the electrodes, altering the relative area between them and thus affecting capacitance. Additionally, capacitance-based sensors can utilize different dielectric materials, each with its own response to external force or deformation (Figure 5g).207 When subjected to force, these materials exhibit varying changes in their effective dielectric constant, leading to fluctuations in capacitance. \n\nTriboelectric sensors operate on the principle of the triboelectric effect, which generates electrical charge when two materials come into contact and then separate. This effect has been harnessed in various contact modes, including vertical contact mode (Figure 5h),208 single electrode mode (Figure 5i),209 sliding mode (Figure 5j),210 and freestanding mode (Figure 5k).211 In each mode, mechanical stimuli cause contact and separation between materials, leading to the generation of electrical signals that can be detected and measured. Triboelectric sensors have found applications in touch and pressure sensing, where they excel in converting mechanical stimuli into electrical signals for further processing and analysis. \n\nIn addition to resistance, capacitance, and triboelectric mechanisms, other approaches have been explored for tactile sensing. Piezoelectric sensors utilize specific materials to generate electric charges in response to mechanical stress, leveraging the asymmetric arrangement of atoms or molecules in their crystal structure (Figure 5l).212 Optical-based sensors measure changes in light intensity or wavelength caused by deformation, offering advantages in terms of precision and stability (Figure $\\mathrm{5m}\\mathrm{\\overline{{\\Omega}}}$ ).118 Magnetic tactile sensors, on the other hand, employ magnetic fields to detect and measure tactile interactions or pressure, often integrating magnetized materials or magnetic field sensors into a substrate (Figure 5n).213 Each mechanism has its own set of advantages and is suitable for different applications based on factors like sensitivity, response time, and environmental conditions. \n\n \nFigure 6. Different kinds of materials for piezoresistive pressure sensors. $(\\mathsf{a}\\mathrm{-}\\mathsf{d})$ Piezoresistive pressure sensor based on metal materials, such as liquid metal (a), AuNWs (b), CuNWs (c), and conductive yarn (d). Reproduced with permission from ref 214. Copyright 2019 Wiley. Reproduced with permission from ref 215. Copyright 2014 Springer Nature. Reproduced with permission from ref 216. Copyright 2022 Elsevier. Reproduced with permission from ref 217. Copyright 2022 ACM. (e−h) Resistance change based on carbon materials, such as graphene (e), SWCNT (f), CNT/CB $\\mathbf{\\eta}(\\mathbf{g})$ , and Velostat (h). Reproduced with permission from ref 218. Copyright 2015 Springer Nature. Reproduced with permission from ref 219. Copyright 2023 Elsevier. Reproduced with permission from ref 220. Copyright 2023 Elsevier. Reproduced with permission from ref 125. Copyright 2018 American Association for the Advancement of Science. (i−l) Resistance change based on polymers, for example, PAAM/PVA hydrogel (i), ACC/PAA/alginate hydrogel (j), PDA@CNT/PAM hydrogel (k), and PEDOT:PSS (l). Reproduced with permission from ref 221. Copyright 2023 Elsevier. Reproduced with permission from ref 222. Copyright 2017 Wiley. Reproduced with permission from ref 223. Copyright 2023 Elsevier. Reproduced with permission from ref 224. Copyright 2019 American Association for the Advancement of Science. \n\n2.1.1. Piezoresistive. In recent years, piezoresistive sensors have witnessed significant advancements across diverse material platforms, notably encompassing metal-based, carbonbased, and polymer-based materials. Each material type brings distinct properties and functionalities to the table, unlocking novel capabilities for a wide range of applications. Metal-based materials have emerged as promising candidates for the fabrication of sensitive and robust resistive pressure sensors, offering significant advancements in healthcare monitoring and wearable devices. Liquid metal is one candidate material, which is famous by its high mobility and conductivity. A 3D-printed rigid microbump-integrated liquid metal-based soft pressure sensor (3D-BLiPS) combines multimaterial fused deposition modeling to achieve a one-step, direct process fabrication (Figure 6a).214 This sensor demonstrates exceptional sensitivity enhanced by a microbump array and excellent robustness to multidirectional stretching, temperature changes, and water immersion. Other common metals, like Au and $\\mathrm{Cu}$ , were applied with novel structures, such as nanowires. A flexible pressure sensor based on ultrathin Au nanowires embedded in tissue paper enables real-time monitoring of blood pulses and detection of small vibration forces (Figure 6b).215 Cu nanowires have also been integrated with cotton fibers to fabricate superhydrophobic piezoresistive pressure sensors, suitable for flexible electronics applications even in humid environments (Figure 6c).216 Moreover, a conductive yarn based on stainless steel was used to detect pressure and integrated into soft pneumatic actuators, presenting a costeffective and robust solution for designing sensing-integrated actuators in assistive wearables and robotics (Figure 6d).217 \n\nCarbon-based materials address the need for high sensitivity and wide pressure range detection in electronic skin and tactile sensing systems, including graphene, carbon nanotube (CNT), and carbon black (CB). For example, laser-scribed graphene (LSG) was utilized to fabricate a flexible and ultrasensitive pressure sensor with a foam-like structure (Figure 6e).218 Other structures, like micropyramidal structure inspired by human skin, have also been explored (Figure 6f).219 It can identify the hardness values of different objects, enabling precise detection of external information in human−machine interaction scenarios. In addition, a washable piezoresistive pressure sensor using porous CNT/CB sponge offers a practical solution for wearable devices (Figure 6g).220 This sensor exhibits excellent compression cycle stability, making it suitable for human motion monitoring and foot membrane inflammation prevention. Moreover, a skin-like driving system enables the compact and reversible assembly of fully soft robots, addressing the compliance gap in existing models (Figure 6h).125 By integrating electronic skins with wireless interskin communication, this system enables untethered, reversible assembly of driving capability, paving the way for highly compact soft robotic designs with minimized inherent hardness and universal robotic actuation. \n\n \nFigure 7. Various structures applied in capacitive pressure sensors. $\\left(\\mathsf{a}-\\mathsf{c}\\right)$ Porous structures with different materials, including GNPs/WMCNTs/ SR/PS (a), PDMS (b) and nanocomposite (c). Reproduced with permission from ref 225. Copyright 2019 American Chemical Society. Reproduced with permission from ref 226. Copyright 2016 Wiley. Reproduced with permission from ref 227. Copyright 2021 Wiley. $\\left(\\mathrm{d-f}\\right)$ Pyramid or dome structures with different materials, such as PVDF (d), polyurethane (e), and parylene (f). Reproduced with permission from ref 228. Copyright 2016 Wiley. Reproduced with permission from ref 229. Copyright 2020 Wiley. Reproduced with permission from ref 230. Copyright 2018 Wiley. $\\mathrm{(g-i)}$ Pillar or cone structures with different materials, like PDMS/CIP (g), PDMS/NOA (h), and P(VDF-TrFE) (i). Reproduced with permission from ref 231. Copyright 2020 Elsevier. Reproduced with permission from ref 232. Copyright 2022 American Association for the Advancement of Science. Reproduced with permission from ref 233. Copyright 2019 Wiley. (j−l) Hybrid or hierarchical structures with various materials, including PU (j), PDMS (k), and CNT/PDMS (l). Reproduced with permission from ref 234. Copyright 2018 American Association for the Advancement of Science. Reproduced with permission from ref 235. Copyright 2019 American Chemical Society. Reproduced with permission from ref 236. Copyright 2021 Wiley. \n\nPolymer-based materials have emerged as promising candidates for flexible and biocompatible pressure sensors, especially hydrogels. These materials offer versatility and compatibility with various applications in healthcare monitoring, artificial intelligence, and wearable devices. For instance, polyacrylamide (PAAm)/poly(vinyl alcohol) (PVA) hydrogels were decorated with pyramid microarrays to detect pressure with high sensitivity and low detection limit (Figure 6i).221 Furthermore, bioinspired mineral hydrogels have constituted the mechanically adaptable ionic skin sensors capable of sensing subtle pressure changes, such as a gentle finger touch or human motion (Figure 6j).222 Another potential benefit comes from hydrogels is self-healing property, such as polyacrylamide (PAM) nanocomposite hydrogels (Figure 6k).223 Moreover, a scalable communication architecture known as the asynchronously coded electronic skin (ACES) has been introduced to enable the asynchronous readout of thousands of tactile sensors through a single conductor (Figure 6l).224 This architecture enables rapid tactile perception and sensor arrays that are dynamically reconfigurable, facilitating applications in artificial intelligence-enhanced autonomous robots and prosthetics. \n\n2.1.2. Capacitive. Capacitive tactile sensors are known for their fast response and wide dynamic detection range, but they often suffer from interference and noise. In the basic parallel plate model, three main factors determine capacitance: the dielectric constant of the materials, the relative area of the electrodes, and the distance between electrodes. Therefore, the design principles of capacitive tactile sensors revolve around materials and structural design. the current status of capacitive tactile sensors is discussed in the following part, from the perspective of structural design. \n\nVarious structural designs have been explored by researchers to enhance the performance of capacitive tactile sensors, especially improving sensitivity and response time (Figure 7). One common type of structures is the porous structure, which undergoes significant deformation under external stimuli (Figure $\\mathrm{7a-\\bar{c},}$ .225−227 For instance, a conductive porous nanocomposite fabricated with carbon nanotubes (CNTs)- doped Ecoflex (Figure 7c), exhibiting $86\\%$ porosity, demonstrated enhance sensitivity (i.e., more than $400\\%$ ) over wide ranges.227 Another prevalent type is the pyramid or dome structure, which concentrates external stress specific locations to amplify the output signal (Figure 7d−f).228−230 An example includes a conductive array integrated with single-walled carbon nanotubes (SWCNTs) and microstructural poly(dimethylsiloxane) (PDMS) (Figure 7f), serving as the electrode of capacitance, contributing to high sensitivity and a linear response.230 \n\n \nFigure 8. Different functional materials for piezoelectric pressure sensors. $\\left(\\mathsf{a}-\\mathsf{c}\\right)$ Inorganic piezoelectric materials, including ZnO $(\\mathsf{a},\\mathsf{b})$ and $\\mathrm{BaTiO}_{3}$ (c). Reproduced with permission from ref 239. Copyright 2014 American Chemical Society. Reproduced with permission from ref 240. Copyright 2016 The Royal Society of Chemistry. Reproduced with permission from ref 241. Copyright 2014 American Chemical Society. (d−f) Organic piezoelectric materials, such as $\\beta$ -Gly/CS film (d), PEDOT ${\\ @\\mathrm{PVDF}}$ fabric (e), and PVDF MNFs (f). Reproduced with permission from ref 242. Copyright 2020 American Chemical Society. Reproduced with permission from ref 243. Copyright 2017 Springer Nature. Reproduced with permission from ref 244. Copyright 2017 IOP Publishing. $\\mathrm{(g-i)}$ Piezoelectric composite materials for pressure sensors, including ppy-PDMS $(\\mathbf{g})$ , BTO/P(VDF-TrFE) nanofibers (h), and PDMS/plastics . Reproduced with permission from ref 245. Copyright 2018 Elsevier. Reproduced with permission from ref 246. Copyright 2019 American Chemical Society. Reproduced with permission from ref 247. Copyright 2012 Wiley. \n\nSimilarly, pillar or cone structure, with larger height-tobottom ratios compared to pyramid or dome structures, have been explored (Figure $\\mathrm{7g-i)}$ .231−233 For instance, inspired by the interlocked microbridges between the epidermis and dermis, a highly sensitive capacitive tactile sensor with interlocked asymmetric nanocones (Figure 7i) demonstrated rapid response time and high sensitivity due to the highly localized stress at the contact apexes.233 Additionally, hybrid or hierarchical structures, combining different microstructures, offer advantages in accommodating diverse tactile sensing requirements (Figure 7j−l).234−236 For example, a biomimetic tactile sensor, combining with pyramid structure and natureinspired phyllotaxis spirals (Figure 7j), resulted in increased sensitivity and excellent stability.234 \n\n2.1.3. Piezoelectric. Piezoelectric pressure sensors operate based on the piezoelectric effect, which generates an electric charge in response to mechanical stress on specific materials like quartz crystals or ceramics. When pressure is applied, it deforms the material’s crystalline structure, redistributing internal electric charges and creating a voltage difference across the material.237,238 This voltage is proportional to the applied pressure, enabling precise measurement. These sensors typically consist of a piezoelectric material sandwiched between two electrodes that collect the generated charges. Recent advancements in materials science have led to the development of novel piezoelectric materials with enhanced sensitivity, flexibility, and biocompatibility. The following overview discusses inorganic, organic, and composite materials for piezoelectric pressure sensors, highlighting their unique properties and applications. \n\nAs for inorganic materials, zinc oxide $(\\mathsf{Z n O})$ and polyvinylidene fluoride (PVDF) are the main candidates. Nanowires (NWs) and nanorods structures have been investigated to enhance the performance of piezoelectricity. A $\\mathrm{{}}Z\\mathrm{{nO}}$ NWs-based touch pad offers a simple yet effective solution for user interfaces (Figure 8a).239 The touch-induced electric charges are converted into voltage outputs, enabling user input for various applications such as programming and gaming. Another flexible self-powered tactile sensor array based on $\\mathrm{{znO}}$ nanorods (Figure 8b).240 Besides ${\\mathrm{{ZnO}}}.$ , PVDF and its composites are promising. A composite thin film, comprising hemispherically aggregated $\\mathrm{\\bfBaTiO}_{3}$ nanoparticles (NPs) and poly(vinylidene fluoride-co-hexafluoropropene) \n\n \nFigure 9. Various electrode materials for triboelectric pressure sensors. $\\left(\\mathsf{a}-\\mathsf{c}\\right)$ Metal electrode materials including AgNWs $(\\mathfrak{a},\\mathfrak{c})$ and $\\mathtt{C u}$ (b). Reproduced with permission from ref 250. Copyright 2020 Wiley. Reproduced with permission from ref 251. Copyright 2016 Wiley. Reproduced with permission from ref 252. Copyright 2020 American Association for the Advancement of Science. (d−f) Semiconductor electrode materials for triboelectric pressure sensors, including CNTs (d), MWCNTs (e), and graphene (f). Reproduced with permission from ref 253. Copyright 2021 Wiley. Reproduced with permission from ref 254. Copyright 2018 Wiley. Reproduced with permission from ref 255. Copyright 2016 Elsevier. $\\mathrm{(g-i)}$ Gel electrode materials for triboelectric pressure sensors, including PVA/PA hydrogel $(\\mathbf{g})$ , MPP-hydrogel (h), and PAMPS ionogel (i). Reproduced with permission from ref 256. Copyright 2022 Elsevier. Reproduced with permission from ref 257. Copyright 2022 Wiley. Reproduced with permission from ref 258. Copyright 2019 Elsevier. (j−l) Hybrid electrode materials for triboelectric pressure sensors, including Ag/PVA nanofibers (j), rGO@AgNWs (k), and PTFE/PS/PET/PA66 (l). Reproduced with permission from ref 259. Copyright 2018 Wiley. Reproduced with permission from ref 260. Copyright 2020 Elsevier. Reproduced with permission from ref 261. Copyright 2022 American Association for the Advancement of Science. \n\nP(VDF-HFP), was utilized to develop high-performance flexible nanogenerators (Figure 8c).241 By employing a solvent evaporation method, the formation of hemispherical BTOP(VDF-HFP) clusters significantly enhances piezoelectric power generation, making them suitable for large-scale fabrication of high-performance flexible nanogenerators. \n\nOrganic piezoelectric materials usually exhibit intrinsic flexibility and compatibility. For example, biodegradable glycine−chitosan piezoelectric films are fabricated through the self-assembly of glycine molecules, exhibiting a sensitivity comparable to nondegradable commercial piezoelectric materials (Figure 8d).242 Another self-powered and wearable electronic skin was designed by weaving polyvinylidene fluoride (PVDF) electrospun yarns of nanofibers coated with PEDOT (Figure 8e).243 For mass production, the printed circuit board technology can be combined (Figure 8f).244 \n\nEncapsulation enables electrical superposition by connecting PVDF micro/nano fibers collectively and effectively in serial/ parallel patterns, achieving high current and voltage output. \n\nOther materials are also promising, including 3D polypyrrole (PPy) network composite with PDMS and $\\mathrm{BaTiO}_{3}$ nanoparticles (Figure 8g),245 barium titanate (BTO)/P(VDFTrFE) composite nanofibers (Figure 8h),246 and $\\mathbf{BaTiO}_{3}$ nanoparticles with graphitic carbons (Figure 8i).247 By dispersing nanoparticles or combining with other nanomaterials in a polymer matrix can enhance the piezoelectric outputs and power generation. \n\n2.1.4. Triboelectric. Triboelectric pressure sensors leverage the triboelectric effect, where electric charge is generated by material contact and separation.248,249 Under pressure, deformation modifies contact area and pressure distribution, causing charge redistribution and signal generation. By employing materials with distinct triboelectric properties, like electron affinities or work functions, these sensors produce charges upon contact and separation. The ensuing signal accurately mirrors the applied pressure, facilitating precise sensing. Ongoing research delves into diverse electrode materials, including metals, semiconductors, gels, and hybrids, aiming to enhance sensor performance, flexibility, and functionality. \n\n \nFigure 10. Magnetic $(\\mathsf{a}-\\mathsf{d})$ and optical (e−f) pressure sensors. (a) Magnetic tactile sensor with bionic hair array. Reproduced with permission from ref 266. Copyright 2024 Wiley. (b) Soft magnetic skin for deformation sensing. Reproduced with permission from ref 267. Copyright 2019 Wiley. (c) Tactile sensor based on magnetic fields. Reproduced with permission from ref 268. Copyright 2019 Springer Nature. (d) Soft magnetic skin for tactile sensing. Reproduced with permission from ref 269. Copyright 2021 American Association for the Advancement of Science. (e) An optical-based 3-axis pressure sensor. Reproduced with permission from ref 270. Copyright 2023 American Association for the Advancement of Science. (f) Optical tactile sensor for haptic perception. Reproduced with permission from ref 271. Copyright 2023 Wiley. \n\nMetal electrode-based TENGs have attracted attention for their robustness and versatility. Bioinspired surface microstructures and polytetrafluoroethylene (PTFE) tinny burrs have enhanced the sensitivity of TENGs to pressure stimuli, enabling applications in robotic tactile sensing and object recognition (Figure 9a).250 Similarly, hemispheres-arraystructured TENGs offer durability in harsh environments and can function as active self-powered sensor arrays for detecting pressure distribution (Figure 9b).251 Not only microstructure, but also nanofibers can be applied. The breathable and biodegradable e-skin, based on silver nanowires and biocompatible polymers, demonstrates real-time monitoring of physiological signals and joint movements, showcasing the potential for healthcare applications (Figure 9c).252 \n\nSemiconductor electrodes are additional choices. Textile triboelectric sensors, leveraging lightweight and mechanically durable materials, offer high-fidelity pulse waveform monitoring validated against traditional blood pressure cuffs (Figure 9d).253 Moreover, paper-based TENGs with multiwalled carbon nanotubes coated air-laid paper electrodes provide washability, breathability, and mechanical stability, making them suitable for integration into wearable devices (Figure 9e).254 The conformal integration of TENGs on human skin enables self-powered touch sensors, facilitating assistive communication systems for individuals with mobility impairments (Figure 9f).255 \n\nGel electrodes offer unique opportunities for human− machine interaction in medical applications, like hydrogels and inongels. Stretchable hydrogel-based TENGs provide selfpowered sensing capabilities, allowing for the transmission of distress calls through finger bending in diagnostic scenario (Figure 9g).256 Another triple-network conductive hydrogel electrodes enhance TENG performance by improving conductivity and electrical output while maintaining mechanical robustness and elasticity, laying the foundation for advanced medical diagnostic tools (Figure 9h).257 In addition, tactile sensors with ionogel electrodes offer high sensitivity for monitoring various human activities (Figure 9i).258 \n\nHybrid electrodes combine the advantages of different materials to achieve highly stretchable and fast rapid response. Ag-nanofiber electrode-based triboelectric sensors enable rapid tactile mapping and detection of various objects, expanding their applications in touchpad technology and interactive interfaces (Figure 9j).259 Multilayered thermoplastic polyurethane (TPU) with silver nanowires (AgNWs) and reduced graphene oxide (rGO) bring about high stretchability (Figure 9k).260 Moreover, smart fingers integrating triboelectric sensing and machine learning enable accurate identification of material type and roughness, offering possibilities for intelligent manipulators and prosthetic devices (Figure 9l).261 The diverse electrode materials explored in triboelectric pressure sensors highlight their potential to revolutionize wearable electronics, human−machine interactions, and medical diagnostics. These advancements underscore the critical role of electrode materials in shaping sensor performance and functionality, paving the way for innovative solutions in tactile sensing and electronic skin technologies. \n\n2.1.5. Other (Magnetic and Optical). There are many other types of pressure sensors or tactile sensors including magnetic and optical sensors.262−265 Magnetic-based pressure sensors have emerged as a promising avenue in the field of tactile sensing, offering significant potential for applications in robotics, healthcare, and object recognition. Magnetic-based pressure sensors operate on the principle of detecting changes in magnetic fields induced by mechanical pressure. These sensors typically consist of a magnet and a magnetic field sensor, such as a Hall-effect sensor, offering several advantages such as high sensitivity and versatility. \n\nTable 1. Summary of Flexible Pressure Sensors: Mechanisms, Main Materials, and Performance \n\n\n<html><body><table><tr><td>Sensing mechanism</td><td> Sensing materials</td><td> Structures</td><td>Sensitivity (kPa-1)'</td><td>Pressure range (kPa)</td><td>Response/recovery time (ms)</td><td>The limit of detection (Pa)</td><td>Cycling stability</td><td> Ref</td></tr><tr><td rowspan=\"9\">Piezoresistive</td><td>Liquid metal</td><td>Microbump</td><td>0.158</td><td>0-50</td><td>77</td><td><16</td><td>10,000</td><td>214</td></tr><tr><td>AuNWs</td><td>Fiber</td><td>>1.14</td><td>0-50</td><td><17</td><td>13</td><td>>50,000</td><td>215</td></tr><tr><td>CuNWs/cotton</td><td>Fiber</td><td>0.15</td><td>1.3-20</td><td>400/300</td><td>N/A</td><td>500</td><td>216</td></tr><tr><td>Graphene</td><td>Foam-like</td><td>0.96</td><td>0-113</td><td>72/0.4</td><td>N/A</td><td>100</td><td>218</td></tr><tr><td>CNT/CB</td><td>Sponge</td><td>164 (0-2.1 kPa)</td><td>0-20</td><td>420/330</td><td>11</td><td>8,000</td><td>220</td></tr><tr><td>PAAm/PVA hydrogel</td><td> Pyramid</td><td>2.27</td><td>0-5</td><td>180/170</td><td>9</td><td>1,000</td><td>221</td></tr><tr><td>P(VDF-TrFe)/ rGO</td><td>Nanofiber</td><td>15.6</td><td>0-55</td><td><5</td><td>1.2</td><td>100,000</td><td>275</td></tr><tr><td>PVDF/PEDOT</td><td>Nanofiber</td><td>18.376 (~100 Pa)</td><td>0.002-10</td><td>15</td><td>2</td><td>7,500</td><td>243</td></tr><tr><td>GNPs/ WMCNTs/SR/ PU</td><td>Porous</td><td>0.062</td><td>0-4.5</td><td>~45</td><td>~3</td><td>2,000</td><td>225</td></tr><tr><td></td><td>PDMS</td><td>Porous</td><td>0.63</td><td>>90</td><td>40</td><td>2.42</td><td>10,000</td><td>226</td></tr><tr><td></td><td>Ecoflex/CNT</td><td>Porous</td><td>3.13 (0-1 kPa)</td><td>0-50</td><td>94</td><td>0.07</td><td>5,000</td><td>227</td></tr><tr><td></td><td>PDMS/SWNT</td><td> Pyramid</td><td>0.7</td><td>0-25</td><td>50</td><td>N/A</td><td>10,000</td><td>230</td></tr><tr><td></td><td>PDMS</td><td> Pillar</td><td>0.301 (0-2 kPa)</td><td>0-200</td><td>~200/~200</td><td>1.2</td><td>5,000</td><td>231</td></tr><tr><td></td><td>PDMS</td><td>Slant hierarchical microstructure</td><td>36000 (0.1-1.2 kPa)</td><td>0-300</td><td>40/70</td><td>0.015</td><td>5,000</td><td>232</td></tr><tr><td></td><td>P(VDF-TrFE)</td><td>Interlocked nanocones</td><td>6.583 (0-0.1 kPa)</td><td>0-1</td><td>48/36</td><td>~3</td><td>10,000</td><td>233</td></tr><tr><td></td><td>PDMS</td><td> Porous pyramid</td><td>44.5 (0-0.1 kPa)</td><td>0-35</td><td>50/100</td><td>0.14</td><td>5,000</td><td>235</td></tr><tr><td rowspan=\"4\">Piezoelectric</td><td>PDMS/CNT</td><td>Gradient microdome</td><td>0.065</td><td>0-1700</td><td><100</td><td>N/A</td><td>7,000</td><td>236</td></tr><tr><td>PAN-C/BTO</td><td>Nanofiber</td><td>1.44 V/N</td><td>0.15-25 N</td><td>N/A</td><td>N/A</td><td>60,000</td><td>276</td></tr><tr><td>P(VDF-TrFE)</td><td>Film</td><td>~0.025V (0-20 kPa)</td><td>0-70</td><td>N/A</td><td><800</td><td>400</td><td>277</td></tr><tr><td>Glycine-chitosan</td><td>Film</td><td>~2.82mV</td><td>5-60</td><td><100</td><td>N/A</td><td>9,000</td><td>242</td></tr><tr><td rowspan=\"5\">Triboelectric</td><td>PDA@BTO/ PVDF</td><td>Film</td><td>~0.775 V/N</td><td>0-250 N</td><td>61</td><td>N/A</td><td>1,000</td><td>278</td></tr><tr><td>PTFE/AgNWs</td><td> Pillar</td><td>127.22 mV</td><td>5-50</td><td>N/A</td><td>N/A</td><td>5,000</td><td>250</td></tr><tr><td>PLGA/AgNWs/ PVA</td><td>Hierarchical porous structure</td><td>0.011</td><td>0-40</td><td>N/A</td><td>N/A</td><td>50,000</td><td>252</td></tr><tr><td>PDMS/PAMPS ionogel</td><td>Triangular stripes</td><td>1.76 V/N</td><td>0.1-1 N</td><td>260 (2 Hz)</td><td>N/A</td><td>6,000</td><td>258</td></tr><tr><td>TPU/AgNWs/ rGO</td><td>Multilayer</td><td>78.4</td><td>0-5</td><td>1.4</td><td>N/A</td><td>10,000</td><td>260</td></tr><tr><td rowspan=\"3\">Optical</td><td>NdFeB/Ecoflex</td><td>Cilia array</td><td>6.63 μT/mN</td><td>0-19.5 mN</td><td>73/81</td><td>N/A</td><td>3,000</td><td>266</td></tr><tr><td>NdFeB/PDMS AuNPs/PDMS</td><td>Film </td><td>0.01</td><td>0-120</td><td>~15</td><td>N/A</td><td>30,000</td><td>269 271</td></tr><tr><td></td><td>Fiber</td><td>3.05 dB/N</td><td>0-4.5 N</td><td>23/27</td><td>2.5 mN</td><td>3000</td><td></td></tr></table></body></html> \n\nOne example is inspired by the bionics of hairs on human skin. magnetic cilia arrays embedded with magnetic particles were utilized to fabricate magnetic sensor arrays (Figure 10a).266 They can detect both magnitude and direction of external forces with resolutions as low as $0.2~\\mathrm{\\mN}$ , and distinguish between different objects based on their magnetic properties. Another magnetic-based tactile skin, utilizing silicone elastomer loaded with magnetic microparticles, is capable of estimating force and localizing contact over large areas with minimal wiring complexity (Figure 10b).267 Moreover, integration of magnetic microelectromechanical systems (MEMS) into electronic skins has enabled bifunctional tactile and touchless perception (Figure 10c).268 they can transduce both mechanical pressure and magnetic field stimuli simultaneously, enabling real-time distinction between tactile and touchless interactions. Distinguish between normal and shear forces have also been achieved by magnetic sensors (Figure 10d).269 These sensors utilize sinusoidally magnetized flexible films and Hall sensors to accurately measure both normal and shear forces, achieving super-resolved accuracy enhanced by deep learning algorithms, offering new possibilities for adaptive grasping and dexterous manipulation. Optical sensors are known for their accuracy and precision.272−274 Thin-film and flexible multipoint 3-axis pressure sensors have been developed using optical methods, enabling high-accuracy sensing of pressure distribution over large areas (Figure 10e).270 By integrating porous rubber as a pressure-sensitive optical modulator, these sensors achieve high sensitivity without sacrificing flexibility or thickness. Furthermore, flexible optical tactile sensors based on soft and plasmonic optical fibers have been introduced, offering sensitive and instantaneous sensing of contact force with low hysteresis and tunable sensitivity (Figure 10f).271 These sensors transduce mechanical stimuli into interpretable light signals, enabling real-time monitoring of pressure and precise perception of object properties. \n\nTable 2. Summary of Performance Results of Recently Reported Stretchable Strain Sensors with Different Types \n\n\n<html><body><table><tr><td>Type</td><td> Materials</td><td>Sensing range</td><td>Gauge factor</td><td>Cyclic ability</td><td>Response time</td><td>Relaxation time</td><td>Hysteresis</td><td> Ref</td></tr><tr><td>Resistive</td><td>MWCNT/natural latex</td><td>200%</td><td>Negative</td><td>2000</td><td>NA</td><td>NA</td><td>NA</td><td>279</td></tr><tr><td>Resistive</td><td>AgNPs/PDMS</td><td>0.65%</td><td>1400 (0-0.46%) 18000 (0.46-0.65%)</td><td>>7000</td><td>258 ms</td><td>247 ms</td><td>NA</td><td>279</td></tr><tr><td>Resistive</td><td>PVA/CA/AgNPs</td><td>596%</td><td>1.6</td><td>200</td><td>90 ms</td><td>240 ms</td><td>0.54% (Residual</td><td>280</td></tr><tr><td>Resistive</td><td>PEDOT:PSS/PVA hydrogel</td><td>300%</td><td>4.07</td><td>2000</td><td>NA</td><td>NA</td><td>strain) <1.5%</td><td>281</td></tr><tr><td>Resistive</td><td>Ionogels</td><td>1000%</td><td>0.83 (0-200%) 1.38 (200-400%)</td><td>300</td><td>200 ms</td><td>300 ms</td><td>0.25%</td><td>282</td></tr><tr><td>Resistive</td><td>Au/carbon black/PDMS</td><td></td><td>1.85 (400-600%) 2420</td><td></td><td></td><td></td><td></td><td>283</td></tr><tr><td>Resistive</td><td>FeO4/carbon black/silicone</td><td>45% 180%</td><td>3.24 (0-120%)</td><td>>12000 9000</td><td>17.5 ms 78 ms</td><td>22.5 ms 65 ms</td><td>NA NA</td><td>284</td></tr><tr><td></td><td>rubber</td><td></td><td>21.985 (120-180%)</td><td></td><td></td><td></td><td></td><td>285</td></tr><tr><td>Capacitive Capacitive</td><td>FPCB electrode/silicone MXene/PVA hydrogel</td><td>30%</td><td>0.52</td><td>1000</td><td>NA</td><td>NA</td><td>3.37%</td><td>286</td></tr><tr><td>Piezoelectric</td><td>PVDF nanoyarn</td><td>200% <10%</td><td>0.4 3.95 V kPa-1</td><td>10000</td><td>190 ms</td><td>160 ms</td><td>NA</td><td>287</td></tr><tr><td>Piezoelectric</td><td>ZnO vertically aligned</td><td>0-420 Torr</td><td>3.15 × 10-² kPa-1</td><td>20000 NA</td><td>50 ms 100 ms</td><td>NA 100 ms</td><td>NA NA</td><td>288</td></tr><tr><td></td><td>nanowire/graphene</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Triboelectric Triboelectric</td><td>Polypropylene yarn PTFE fibers/nylon fibers</td><td>30% 80%</td><td>45.47 V (20-30%) Output 0.5 V (under 1%</td><td>40000 >20000</td><td><100 ms 70 ms </td><td><100 ms 71 ms</td><td>NA NA</td><td>289 290</td></tr><tr><td></td><td></td><td></td><td>stretch strain)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Light Light</td><td>Graphene/PDMS Plasmonic gold NPs/PDMS</td><td>150% 100%</td><td>NA NA</td><td>200 6000</td><td>NA 12 ms </td><td>NA NA</td><td>NA NA</td><td>291 292</td></tr></table></body></html> \n\nIn short conclusion, various types of pressure sensors also contribute significantly to the field of tactile sensing, providing distinctive capabilities and applications in robotics, healthcare, and human−machine interaction (Table 1). Ongoing research and development endeavors continue to drive innovation in tactile sensing technology, offering promising prospects for further advancements and integration into various practical applications.",
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"category": " Results and discussion"
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"chunk": "# 2.2. Strain Sensors \n\nStrain sensors are devices that detect deformation or strain in a material and convert this mechanical deformation into an electrical signal. This deformation can be caused by tension, compression, or shear forces. Within the realm of flexible and stretchable strain sensors, three primary classifications emerge: (i) resistive-type, (ii) capacitive-type sensors, and (iii) piezoelectric and triboelectric sensors. Table 2 summarizes the performance of strain sensors developed recently. \n\nPiezoresistive strain sensors rely on the piezoresistive effect, where the electrical resistance of a material changes when it is deformed. Capacitive strain sensors detect strain by measuring changes in capacitance. When the sensor deforms, the distance between the plates of a capacitor or the dielectric properties of the material changes, leading to a change in capacitance. The third type of strain sensors may utilize the piezoelectric or triboelectric effect, where certain materials generate an electric charge in response to mechanical stress. In addition to the primary types mentioned above, there are several other kinds of strain sensors, including optical fiber strain sensors, which use changes in light properties to detect strain, and resistive foil strain gauges, which are among the most traditional and widely used strain measurement devices. Strain sensors based on magnetic mechanisms also show great potential in emerging applications. These sensors are indispensable tools in modern engineering and technology, providing critical data for robotics. \n\n2.2.1. Resistive. Resistive strain sensors can be categorized into three types based on their functional materials: carbon nanomaterial-based resistive strain sensors, metal-based resistive strain sensors, and those utilizing other materials. These sensors typically consist of electrically conductive sensing films integrated with flexible substrates. When composite structures undergo deformation, microstructural changes within the sensing films lead to variations in electrical resistance corresponding to the applied strain. Upon strain release, the sensing films revert to their initial configurations, restoring the electrical resistance of the sensors.293 Representative resistive strain sensors are discussed below. \n\nCNTs are commonly used as conductive materials in resistive strain sensors. A transparent stretchable strain sensor based on single-walled carbon nanotubes (SWCNTs) is reported by Wang et al. (Figure 11a).294 The SWCNT layer was transferred to a PDMS film for a highly transparent stretchable strain sensor with a uniform patterned sensing layer. Yamada et al. presented a category of wearable and stretchable devices constructed using thin films composed of aligned SWCNTs, as shown in Figure $116^{295}$ These SWCNTs film devices, designed for characterizing strain sensors, were fabricated on a dog-bone-shaped substrate composed of PDMS, enabling strain measurements of up to $280\\%$ . In another research, graphene is also used as functional materials (Figure 11c).204 A conductive nanonetwork composed of graphene nanoribbons (GNRs) was established on the surfaces of electrospun TPU fibrous membranes, enabling precise sensing of human motion. This achievement was validated through training sessions with elite dragon boat paddlers. \n\nIn terms of metal-based resistive strain sensors, Bai et al. fabricated epidermal fabric strain sensors with Au coated TPU fibers, as shown in Figure 11d.296 The effects of prestretch direction and plating strain on sensor response are examined. Additionally, the response of the sensor, including its measuring range and sensitivity, can be controlled during the fabrication process. Another resistive strain sensor was reported to fabricated using core−shell Ag@Au structures with PU (Figure 11e).297 The nanomesh-design of a strain sensor is capable of direct printing onto hands. It imitates human cutaneous receptors by converting electrical resistance change caused by gentle skin stretches into proprioceptive sensations. Besides, Liquid metals (gallium−indium alloy) are commonly used as an intrinsic resistive strain sensor, such as patterned Ga−In strain sensor on gloves (Figure 11f)298 and Ga−In tattoo (Figure 11g).299 \n\n \nFigure 11. Resistance-type flexible strain sensor for soft robotics, classified by the types of active materials. Strain sensors based on carbon nanomaterials, including (a, b) SWCNTs and (c) Graphene. Reproduced with permission from ref 294. Copyright 2020 Springer Nature. Reproduced with permission from ref 295. Copyright 2011 Springer Nature. Reproduced with permission from ref 204. Copyright 2020 Springer Nature. Strain sensors based on metal materials, including (d) Au, (e) Ag and Au, and $\\left(\\mathrm{f{-}g}\\right)$ liquid metal. Reproduced with permission from ref 296. Copyright 2023 Wiley. Reproduced with permission from ref 297. Copyright 2023 Springer Nature. Reproduced with permission from ref 298. Copyright 2020 Wiley. Reproduced with permission from ref 299. Copyright 2021 American Association for the Advancement of Science. Resistive-type strain sensors based on other materials including $\\left(\\mathrm{h-j}\\right)$ conductive polymer, $(\\mathrm{j,k}$ and $^{1,\\mathrm{{m}}}.$ ) composite, and (n) MXene. Reproduced with permission from ref 300. Copyright 2022 Wiley. Reproduced with permission from ref 281. Copyright 2022 Wiley. Reproduced with permission from ref 301. Copyright 2017 Wiley. Reproduced with permission from ref 302. Copyright 2020 American Association for the Advancement of Science. Reproduced with permission from ref 303. Copyright 2017 Wiley. Reproduced with permission from ref 304. Copyright 2021 Wiley. Reproduced with permission from ref 305. Copyright 2023 Wiley. \n\nOther functional materials including hydrogels, composites, and nanomaterials are widely used in resistive strain sensors. Taking the benefit of hydrogels’ tissue-like softness, Yao et al. introduced a novel phenylboronic acid-ionic liquid (PBA-IL) monomer (Figure 11h)300 The incorporation of multiple crosslinking networks, including dynamic covalent bonds (boronic ester bonds), physical interactions (hydrogen bonds and electrostatic interactions), and chain entanglement allows the as-prepared PAM/PBA-IL/CNF hydrogel to achieve a favorable balance between high performance and multifunctionality. Minimize response hysteresis of strain is challenging, especially when using polymer as substrates. To solve this, Shen et al. presented a PEDOT:PSS-PVA hydrogel strain sensor. Owing to the microphase semiseparated network, the hydrogel strain sensor shows large sensing range $\\left({>}300\\%\\right)$ and ultralow hysteresis (Figure 11i)281 Another hydrogel-based strain sensor, achieve long-lasting moisture and extreme temperature tolerance (Figure 11l).303 Different application scenarios require the strain sensors to attain different properties. To ensure the hydrogel-based strain sensor showed no expansion under water, Ren et al. reported an antiswellable, and ionic conductive hydrogel (DN-FT-HCl) for resistive strain sensors, as shown in Figure $\\scriptstyle11{\\mathrm{m}}$ .304 The increased hydrogen bonding facilitated by the flexible poly(hydroxyethyl methacrylate) chains confers resistance to deformation in the hydrogel. In addition, conducting polymer composite as resistive strain sensor are also reported (Figure 11j)301 This composite comprises three key components: polyaniline (PANI), poly(acrylic acid) (PAA), and phytic acid (PA). The cross-links between polymers form a robust and flexible material which make the composite an ideal material for electronic skins. CB/PDMS composite was proposed to fabricated all-soft robotic textiles with sensing abilities (Figure $11\\mathbf{k})^{302}$ Moreover, nanomaterials other than carbon nanomaterials are also ideal candidates for strain sensor, among which MXene stands out with its high conductivity. Zhao et al. introduced a MXene nanoflake-based hydrogel, as shown in Figure $11\\mathrm{n}^{305}$ Due to the properties of MXene nanoflakes, including hydrophilicity, abundant surface functional groups, and high specific surface area, alongside hydrogen bonding and electrostatic interactions among the components, hydrogels demonstrate exceptional mechanical properties, conductivity, and water retention. \n\n \nFigure 12. Capacitive-type flexible strain sensor for soft robotics, classified by the types of active materials: Carbon-based capacitive strain sensors, for example (a) CNT and (b) rGO. Reproduced with permission from ref 306. Copyright 2022 Wiley. Reproduced with permission from ref 307. Copyright 2022 American Chemical Society. Metal-based capacitive strain sensors, for example $^{\\mathrm{(c,d)}}$ liquid metal and (e) AgNWs. Reproduced with permission from ref 308. Copyright 2022 Wiley. Reproduced with permission from ref 309. Copyright 2023 Springer Nature. Reproduced with permission from ref 310. Copyright 2023 Wiley. Polymer-based capacitive strain sensors using gels. (f) Hydro/Organo-Gels. (g) Organogel/ Hydrogel. Reproduced with permission from ref 311. Copyright 2021 Wiley. Reproduced with permission from ref 312. Copyright 2023 American Chemical Society. \n\n2.2.2. Capacitive. Other than resistive flexible strain sensor, capacitive ones utilize a highly compliant dielectric layer positioned between two stretchable electrodes. Tensile strain brings the electrodes into closer proximity, leading to a rise in capacitance. \n\nFor highly sensitive and stretchable strain sensors, high linearity and low hysteresis are especially challenging. Focusing on improving the dynamic properties of strain sensor, Hu et al. reported a superstretchable and highly sensitive capacitive strain sensor, composed of two strips of wrinkled carbon nanotube-based electrodes separated by a dielectric tape (Figure 12a).306 The sensor was manufactured by transferring carbon nanotube film electrodes, formed through spraycoating, onto both sides of prestretched VHB elastomer, thereby significantly reducing costs. Through the integration of nanomaterials and a wrinkled film structure, this device achieves a gauge factor of 2.07 at $300\\%$ strain, demonstrating excellent linearity and negligible hysteresis. In the meanwhile, ultrasensitivity is also required in some scenarios. In another work by Jiang et al., an exceptionally stretchable and selfhealing hydrogel conductor was developed through a strategy employing both hard and soft dynamic networks (Figure 12b).307 This approach involved the integration of conductive silver nanowire assemblies with wrinkled reduced graphene oxide (RGO) nanosheets within an $\\mathrm{\\Ag{-}}S$ coordinationassisted polyacrylamide hydrogel network. The resulting hydrogel exhibited outstanding performance compared to previously reported stretchable conductors, demonstrating a remarkable elongation of $3250\\%$ . \n\n \nFigure 13. Flexible strain sensors with other mechanisms. Piezoelectricity-based strain sensors: (a) inorganic tensile strain sensors, (b) strain sensor with hybrid working mechanism, (c) Kirigami strain sensor. Reproduced with permission from ref 316. Copyright 2022 Springer Nature. Reproduced with permission from ref 315. Copyright 2022 Elsevier. Reproduced with permission from ref 314. Copyright 2022 Springer Nature. Triboelectricity-based strain sensors: (d) fiber-shaped strain sensors, (e) triboelectricity-based sensors for gesture detection, (f) triboelectric sensors for lip motion. Reproduced with permission from ref 290. Copyright 2022 American Chemical Society. Reproduced with permission from ref 317. Copyright 2020 Springer Nature. Reproduced with permission from ref 318. Copyright 2023 Springer Nature. Light-based strain sensors, including (g) optical waveguides and $\\mathrm{(h)}$ optical fibers. (i) Magnetic-based strain sensors. Reproduced with permission from ref 118. Copyright 2016 American Association for the Advancement of Science. Reproduced with permission from ref 319. Copyright 2018 Wiley. Reproduced with permission from ref 320. Copyright 2022 American Chemical Society. \n\nDickey et al. presented a LM-interdigitated capacitive strain sensor (LMICSS) comprising a polydimethylsiloxane (PDMS) microfluidic network filled with LM, depicted in Figure 12c.308 \n\nAs anticipated, capacitance diminished as strain increases. The sensor exhibits high stretchability $(100\\%)$ with a gauge factor of $-0.3$ and excellent durability. Moreover, it demonstrated minimal hysteresis $(<1\\%)$ and is free from crosstalk between strain and normal stress sensing, attributable to its coplanar electrode microchannel configuration. In another work, Fu et al. introduced a $\\mathrm{Ga-In}$ wrapped with thin oxide layer aiming at overcoming the trade-off between soft self-healing properties and high fracture toughness, thus enabling the transformation of soft and weak materials into ones that are both soft and tough while possessing self-healing capabilities (Figure 12d).309 This distinctive approach, inspired by the structure of vascular smooth muscle, was achieved through a hierarchical design involving the integration of core−shell structured spindle Galinstan microdroplets into a soft self-healing polyurea matrix via molecularly interfacial metal-coordinated assembly. The resulting composite exhibited remarkable enhancements in crack-resistant strain and fracture toughness. AgNWs are reported to be used as functional materials in another capacitive strain sensor design. To solve the interference problem, Pan et al. presented a highly antijamming capacitive flexible pressure sensor utilizing a polyvinylidene fluoride (PVDF) $(\\varpi\\mathrm{AgNW}s@\\mathrm{TiO}_{2}$ film as the dielectric layer. The PVDF film incorporates $\\mathsf{A g N W s}@\\mathrm{TiO}_{2}$ with a core−shell structure. This incorporation not only boosts the initial capacitance but also aligns the dielectric constant, dielectric loss, and breakdown strength of the dielectric layer, as shown in Figure 12e.310 \n\nCapacitive-type strain sensors utilizing hydrogel ionic conductors have experienced rapid advancement owing to their resilient structure, drift-free sensing capability, heightened sensitivity, and precision. The electro-mechanical stability of conventional hydrogel conductors, typically susceptible to significant deformation and harsh mechanical forces, continues to pose a challenge. To obtain robust capacitive strain sensor, Mo et al. introduced a dynamically supertough capacitive-type strain sensor based on energy-dissipative dual-cross-linked hydrogel conductors and an organogel dielectric with high adhesive strength (Figure 12f).311 Leveraging the mechanical benefits of the hydrogel and organogel materials, the strain sensor demonstrated exceptional stretchability and a superior linear sensitivity with a gauge factor of approximately $0.8\\%$ at $100\\%$ strain. Furthermore, the sensor exhibited remarkable stability against severe mechanical stresses, enduring even when subjected to extreme scenarios such as being run over by a car on 20 occasions. Utilizing the observed phenomenon of minimal volume change during solvent replacement in the transition from organogels to hydrogels, Zhou et al. designed distinct types of capacitive and resistive strain sensors through various organogel/hydrogel hybrids featuring intricate patterns.312 By relying on the solvent replacement area for pattern formation, we could conveniently fabricate flexible electronic sensors based on organogel/hydrogel hybrids, featuring complex topological structures and functionalities. \n\n2.2.3. Piezoelectric and Triboelectric. Piezoelectric and triboelectric devices also show their great potential in flexible strain sensors, for effective harvesting of instant mechanical energy to create self-powered systems .313 Bending sensing is a highlight topic in soft robotics and human−machine interaction (HMI), where accuracy of detection of angle change is important. Kim et al. developed a high-performance kirigami piezoelectric strain sensor, evaluating its sensing performance through finite element analysis and optimizing the kirigami patterns (Figure 13c).314 The electromechanical properties of sensors featuring four distinct kirigami patterns analyzed. The piezoelectric strain sensor showed voltage measurement circuit that improved measurement accuracy by amplifying the output voltage 86.5 times. Hybrid systems were developed through both piezoelectric and triboelectric mechanisms to meet the needs of some scenarios. For example, Wang et al. introduced an autonomous wake-up wireless sensing approach utilizing a hybrid generator in wearable bending (Figure 13b).315 Unlike continuous data recording and transmission, the TENG signal triggered the recording of PENG voltage amplitude, serving as angle sensing data for wireless transmission. This concise and precise sensing method with autonomous wake-up is anticipated to mitigate computational load and power consumption in wireless sensing, offering greater potential for wearable wireless monitoring and human−computer interaction. With the rapid development of material science and nanotechnology, a new sensing mechanism based on traditional piezoelectric mechanism was developed, which was called piezotronics effect. The piezotronic effect harnesses the piezoelectric potential generated in materials with piezoelectric properties to act as a “gate” voltage, thereby regulating the charge carrier transport properties to fabricate novel devices. By leveraging the coupling between piezoelectricity and the transport properties of semiconductors, piezotronic sensors utilize strain-induced piezoelectric polarization charges and the resulting piezoelectric potentials at interfaces to linearly adjust the interface barrier height and exponentially control carrier transport. An optimized piezotronic tunneling strain sensor is developed using the $\\mathrm{Ag/HfO_{2}/n\\mathrm{-}Z n O}$ structure (Figure 13a).316 Piezotronic modification effects on interface tunneling transport are observed to occur in two distinct stages: ultralow strain $\\left(<0.01\\%\\right)$ and relatively high strain $\\left(0.01{-}0.10\\%\\right)$ . These stages correspond to the predominant influence of piezotronic regulation on barrier width and barrier height, respectively. \n\nIn a wearable sign-to-speech translation system, a triboelectric sensing array was integrated on a glove for real-time gesture attraction (Figure 13e).317 A coiled structural configuration ensured that the yarn-based stretchable sensing unit maintains adequate electrical conductivity even when subjected to extreme stretching, thereby ensuring the exceptional durability of the sensing units. The system achieved a recognition rate of $98.63\\%$ and completes recognition in less than 1 s. Structural engineering is important in triboelectric device design. A helical fiber strain sensor (HFSS) was developed by incorporating a helical structure onto a stretchable substrate fiber (Figure 13d).290 Unlike other stretchable fiber strain TENGs, HFSSs fully leverage their helical design. Even with minor stretching, alterations in the contact state between the two triboelectric layers (PTFE and nylon) generate a significant electrical signal. In another application, Lu et al. introduced a novel lip-language decoding system (LLDS) designed to capture mouth muscle movements using flexible, low-cost, self-powered sensors and to recognize signals through a deep learning classifier (Figure 13f).318 These self-powered sensors were strategically positioned at the junction of mouth muscles and were fabricated using flexible polymer films to enhance skin sensation in the mouth region. To address challenges related to signal diversity and limited sample size, they employ a dilated recurrent neural network model based on prototype learning. The system achieved a testing accuracy of $94.5\\%$ . \n\n2.2.4. Other. In addition to the aforementioned mechanisms, strain sensors based on light and magnetism are emerging and provide instructive ideas for strain sensor designs. Zhao et al. introduced the utilization of stretchable optical waveguides for strain sensing in a prosthetic hand (Figure 13g).118 These photonic strain sensors, characterized by ease of fabrication, chemical inertness, and minimal hysteresis, exhibited high precision in their output signals. To showcase their potential, they were employed as curvature, elongation, and force sensors integrated within a fiberreinforced soft prosthetic hand. This optoelectronically innervated prosthetic hand was utilized to conduct a range of active sensation experiments inspired by the functionalities of a natural hand. As the soft optical systems exhibited notable versatility, particularly in scenarios involving extensive and repetitive deformations necessitating dynamically responsive materials. A study demonstrated the efficacy of stretchable step-index optical fibers, showcasing their ability to withstand strains of up to $300\\%$ reversibly while effectively guiding light (Figure 13h).319 Employing a continuous and scalable meltflow process, the fibers were fabricated by coextruding two thermoplastic elastomers, thus establishing their core-high index and cladding-low index structure. Deformation of these fibers through stretching, bending, and indentation elicits detectable, predictable, reversible, and wavelength-dependent alterations in light transmission. In existing devices, the presence of wires and power supplies poses inconveniences and potential hazards. Magnetic-based systems have emerged as promising alternatives for wireless and passive sensing; however, their widespread adoption in biomechanical monitoring has been hindered by disparities in mechanical properties, limited biocompatibility, and inadequate sensitivity. Addressing these challenges, researchers have developed a wireless and passive flexible magnetic-based strain sensor using a gelatin methacrylate/ $\\mathrm{\\mathrm{Fe}}_{3}\\mathrm{O}_{4}$ magnetic hydrogel (Figure 13i).320 This sensor boasted ultrasoft mechanical characteristics, robust magnetic properties, and sustained stability in saline environments, enabling the monitoring of strains as low as $50~\\mu\\mathrm{m}$ Additionally, a sensing model had been devised to determine the optimal detection site and establish the relationship between relative magnetic permeability and sensor sensitivity.",
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"chunk": "# 2.3. Temperature Sensors \n\nTemperature sensing technology has advanced significantly in recent years, offering diverse solutions across different domains, from healthcare to robotics. In this review, we categorize temperature sensors into four types: resistive temperature sensors, thermistors, thermocouples, and other emerging technologies. Each type offers unique advantages and applications, contributing to the broad spectrum of temperature sensing capabilities. \n\n \nFigure 14. Temperature sensors with various mechanisms, including resistive temperature sensors $\\left(\\mathsf{a}-\\mathsf{f}\\right)$ , thermistor $^{(\\mathrm{g,h)}}$ , thermocouple (i,j), and others $\\mathbf{\\Phi}(\\mathrm{k},\\mathrm{l})$ . (a) Soft electronic arrays for sensing and actuation. Reproduced with permission from ref 321. Copyright 2020 Springer Nature. (b) Multifunctional integumentary membranes for spatiotemporal cardiac measurements and stimulation. Reproduced with permission from ref 322. Copyright 2014 Springer Nature. (c) PEDOT:PSS-based temperature sensor for health monitoring. Reproduced with permission from ref 323. Copyright 2020 Springer Nature. (d) Wearable temperature sensor based on graphene nanowalls. Reproduced with permission from ref 324. Copyright 2015 The Royal Society of Chemistry. (e) Stretchable and conformable networks for multifunctional sensing. Reproduced with permission from ref 325. Copyright 2018 Springer Nature. (f) Skin temperature detection based on transparent and flexible fingerprint sensor array. Reproduced with permission from ref 326. Copyright 2018 Springer Nature. (g) Hydrogel-based ionic skin for a soft robotic gripper. Reproduced with permission from ref 327. Copyright 2020 The Royal Society of Chemistry. (h) Wireless soft sensors for continuous temperature measurement. Reproduced with permission from ref 328. Copyright 2021 Springer Nature. (i) Flexible temperature sensor for functional microfingers. Reproduced with permission from ref 329. Copyright 2019 Springer Nature. (j) Stretchable thermoelectric fabric for wearable temperature sensors. Reproduced with permission from ref 330. Copyright 2018 The Royal Society of Chemistry. (k) Stretchable temperature sensors based on hydrogel films. Reproduced with permission from ref 331. Copyright 2021 American Chemical Society. (l) Stretchable silicon nanoribbon electronics for skin prosthesis. Reproduced with permission from ref 332. Copyright 2014 Springer Nature. \n\n \nFigure 15. Primary working principles of PDs. (a) Photodiodes. (b) Photoresistors. (c) Phototransistors. Reproduced with permission from ref 335. Copyright 2024 American Chemical Society. Inorganic PDs, including (d) 0D nanomaterials, (e) 1D nanomaterials for artificial retina, (f) 2D nanomaterials. Organic PDs: (g) organic RGB RDs, (h) optoelectronic synapse, and (i) synaptic transistors. Reproduced with permission from ref 339. Copyright 2020 Wiley. Reproduced with permission from ref 340. Copyright 2020 Springer Nature. Reproduced with permission from ref 341. Copyright 2023 Springer Nature. Reproduced with permission from ref 342. Copyright 2021 Wiley. Reproduced with permission from ref 343. Copyright 2023 Springer Nature. Reproduced with permission from ref 344. Copyright 2022 Springer Nature. Structural integrated photonic sensor systems with (j,k) origami designs, (l) kirigami design. $\\scriptstyle{\\left({\\mathrm{m},\\mathrm{n}}\\right)}$ island-bridge design, (o) pop-up design, and (p) Fiddler crab eye mimicking artificial vision system. (q) Aquatic-vision-inspired camera. Reproduced with permission from ref 345. Copyright 2017 Springer Nature. Reproduced with permission from ref 346. Copyright 2019 Wiley. Reproduced with permission from ref 347. Copyright 2021 Springer Nature. Reproduced with permission from ref 348. Copyright 2019 Springer Nature. Reproduced with permission from ref 349. Copyright 2013 Springer Nature. Reproduced with permission from ref 350. Copyright 2018 Springer Nature. Reproduced with permission from ref 351. Copyright 2022 Springer Nature. Reproduced with permission from ref 352. Copyright 2020 Springer Nature. \n\n2.3.1. Resistive type. Resistive temperature sensors have garnered significant attention due to their simplicity, versatility, and reliability in various applications. These sensors operate based on the principle that the material resistance changes with temperature. Some of them focus on healthcare applications. For minimally invasive cardiac surgery, the soft multilayer electronic arrays were integrated into endocardial balloon catheters (Figure 14a).321 These arrays enable high-density spatiotemporal sensing and actuation, facilitating precise mapping of temperature, pressure, and electrophysiological parameters during surgical procedures. For physiological mapping and stimulation, 3D elastic membranes shaped to match the epicardium of the heart offer deformable arrays of multifunctional sensors and components, providing comprehensive coverage of the heart’s surface (Figure 14b).322 Furthermore, advancements in printed flexible temperature sensors, such as those based on cross-linked PEDOT:PSS, offer enhanced stability and sensitivity, making them suitable for real-time healthcare monitoring in wearable applications (Figure 14c).323 Graphene nanowalls-based wearable temperature sensors exhibit high sensitivity, fast response/recovery speed, and long-term stability, positioning them as valuable tools for personalized healthcare systems (Figure 14d).324 Other temperature sensors pay attention to the robotics or devices. Inspired by human skin, highly stretchable sensor matrix networks enable multifunctional sensing, including temperature detection (Figure 14e).325 Also, lastly, transparent and flexible fingerprint sensor arrays offer multiplexed detection of tactile pressure and finger skin temperature, integrating seamlessly into mobile smart devices (Figure 14f).326 \n\n2.3.2. Thermistor. Thermistors, temperature sensors renowned for their sensitivity to temperature fluctuations, have witnessed recent advancements aimed at overcoming challenges posed by extreme environmental conditions and facilitating continuous temperature monitoring at vital interfaces. For instance, temperature sensors based on zwitterionic PIL hydrogels exhibit remarkable superstretchability, self-healing capabilities, and high conductivity, ensuring stable sensing across a broad temperature range (Figure $\\mathrm{14g)}$ .327 Moreover, soft, skin-mountable sensor systems have emerged to enable uninterrupted monitoring of pressure and temperature at crucial skin interfaces, showcasing feasibility and functionality in hospital settings. These systems hold promise for enhancing patient care and may find diverse applications in healthcare (Figure 14h).328 \n\n2.3.3. Thermocouple. Thermocouples, composed of two dissimilar metal wires fused together, generate a voltage proportional to the temperature variance between the two junctions. Recent advancements in thermocouples have spurred innovations in flexible microactuator-integrated temperature sensors, featuring functional microfingers with temperature sensing capabilities. These sensors exhibit reliability amidst actuation effects and offer potential for applications necessitating flexible and precise temperature measurements (Figure 14i).329 Moreover, textile-based selfpowered temperature sensors, crafted using commercial thermoelectric inks, present linear temperature-sensing capabilities, and high durability. These sensors are well-suited for human−machine interfaces and health monitoring applications (Figure 14j).330 \n\n2.3.4. Other. In addition to the aforementioned advancements, emerging technologies are reshaping the landscape of temperature sensing, offering novel solutions with enhanced capabilities and versatility. One such innovation is the development of stretchable transparent temperature sensors, which employ a novel thin-film sandwich structure (Figure 14k).331 These sensors represent a significant breakthrough as they allow for comfortable attachment to human skin while enabling reliable monitoring of diverse environmental conditions. By relying on changes in capacitance rather than conventional methods, these sensors offer improved accuracy and responsiveness, making them ideal for applications requiring precise temperature measurements in wearable electronics, healthcare, and environmental monitoring. \n\nMoreover, the integration of smart prosthetic skins equipped with ultrathin, single crystalline silicon nanoribbon sensors marks another notable advancement in temperature sensing technology (Figure 14l).332 These sensors provide highly localized mechanical and thermal perception, mimicking the sensitivity of human skin. As a result, they offer new possibilities for prosthetic and sensory interface technologies, enhancing the functionality and user experience of prosthetic limbs and wearable devices. \n\nIn conclusion, the evolution of temperature sensing technology is driven by continuous advancements in materials, fabrication techniques, and integration methods.333 From flexible and wearable sensors to sophisticated prosthetic skins, temperature sensors are becoming increasingly sophisticated and versatile, enabling a wide range of applications across various industries.",
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"chunk": "# 2.4. Optical Sensors \n\nA photodetector functions as a sensor, detecting light and converting photons into an electrical signal. These devices are ubiquitous in daily life, employed in optical telecommunications, imaging, and biomedical sensing.334 In recent years, flexible photodetector systems have been investigated in robotic applications, such as e-eyes, and flexible cameras.335−337 \n\n2.4.1. Mechanism and Materials. Photodetectors (PDs) convert incident light into electrical signals, primarily utilizing either the photoconductive or photovoltaic effect. The photoconductive effect occurs when absorbed photons generate additional free carriers, reducing semiconductor resistance. Under low light conditions, minimal dark currents flow between the source and drain. However, with light absorption, photon energy exceeding the bandgap creates electron−hole pairs, enhancing photocurrent and reducing device resistance. The photogating effect, a variation of the photoconductive process, arises from trapped photogenerated carriers on defects or surfaces.338 \n\nPDs are categorized into three structures: photodiodes, photoresistors, and phototransistors (pTrs). Photodiodes, commonly employed in flexible and stretchable PDs due to their photovoltaic effect, comprise two-terminal electrodes and an active photoresponsive layer (Figure 15a). Typically, vertically stacked, these photodiodes utilize transparent metal top electrodes, such as ITO, enabling incident light penetration to the active layer, generating electron−hole pairs. This vertical design facilitates rapid response times owing to the short distance between photocarriers and electrodes. In contrast, photoresistors (Figure 15b) feature a simpler configuration with source and drain electrodes and an active layer forming two Ohmic contacts. Operating on the photoconductive effect, they exhibit a wide dynamic range despite extended response times. pTrs, a hybrid of transistors and photodiodes, possess three terminals: source, drain, and gate electrodes, along with an active layer (Figure 15c). Structurally resembling conventional TFTs, pTrs employ photoconductive material in lieu of a semiconducting layer. Under illumination, pTrs, akin to photodiodes, generate electron−hole pairs, and a gate voltage modulates induced photocurrents to amplify output signals, achieving higher sensitivity compared to photodiodes. \n\nFlexible PDs can be classified through functional materials: inorganic materials and organic materials. Inorganic materialbased PDs exhibit excellent optoelectronic performance, characterized by high carrier mobility facilitated by advanced fabrication techniques. Nonetheless, the inherent bulkiness, brittleness, and rigidity of inorganic semiconducting materials pose challenges for integrating them into flexible or stretchable optoelectronic devices. Addressing this limitation, nanoscale size reduction is employed to enhance the device’s flexibility while preserving the high-performance attributes of inorganic materials. 0D nanomaterial-based PDs exhibit distinct optical, electrical, and mechanical features influenced by the size and shape of semiconducting nanoparticles (NPs) and quantum dots (QDs). Shen et al. demonstrated a straightforward onepot synthesis of all-inorganic $\\mathrm{Cs}\\mathrm{Pb}{\\mathrm{Br}}_{3}$ quantum dots (QDs) under ambient conditions (Figure 15d).339 Particularly, the CsBr/KBr assisted strategy enhances the electrical and optical properties of the QDs. The resulting arrays display significant folding endurance, electrical stability, and uniform performance. Optoelectronic devices leveraging 1D nanomaterials exploit their optical and electrical properties. The advantageous physical attributes, such as a large surface area-to-volume ratio and wire-like geometry, make them conducive to developing high-quality PDs, extending photocarrier lifetimes, reducing transit times, and enhancing mechanical flexibility. Gu et al. presented the development of an artificial visual system employing a spherical biomimetic electrochemical eye (EC-EYE) featuring a hemispherical retina composed of a densely packed perovskite nanowire array fabricated via vaporphase deposition. The device design closely mimics the structural characteristics of the human eye, offering the potential for achieving high-resolution imaging by individually addressing nanowires electrically (Figure 15e).340 Recently, 2D materials have emerged as functional layers in flexible photodetectors, leveraging their exceptional optoelectronic characteristics and high mechanical flexibility. arrays can directly perceive different types of motion at sensory terminals, emulating the nonspiking graded neurons of insect vision systems. The charge dynamics of the shallow trapping centers in $\\mathbf{MoS}_{2}$ phototransistors mimic the characteristics of graded neurons, showing an information transmission rate of 1200 bit $\\ensuremath{\\mathbf{s}}^{-1}$ and effectively encoding temporal light information. Chen et al. introduced phototransistor arrays capable of directly perceiving various types of motion at sensory terminals, emulating the nonspiking graded neurons found in insect vision systems. The charge dynamics of shallow trapping centers in $\\mathbf{MoS}_{2}$ phototransistors mimic the characteristics of graded neurons, exhibiting an information transmission rate of 1200 bits per second and effectively encoding temporal light information (Figure 15f).341 Organic semiconductors have attracted significant attention for flexible and stretchable PDs owing to their distinctive physical, optical, and electrical properties. These include intrinsic flexibility and stretchability, a broad response range, low fabrication cost, lightweight construction, and high compatibility with other electronic components. Moreover, the tunable characteristics of organic semiconductors through chemical design offer additional advantages for the development of customized devices, as the smart artificial retina exhibited in Figure 15g−i.342−344 \n\n2.4.2. Integrated Photonic Systems. Sensing is vital for the survival of most animals, necessitating the processing of vast amounts of external information from visual, auditory, and tactile stimuli. Visual data, in particular, constitutes a substantial portion of this information, driving the development of artificial vision systems. The camera, comprising multiple lenses and a planar image sensor, constitutes a fundamental component of these systems. Recent advancements in electronics and mechanics enable modern machines and robots to perform diverse tasks using various sensing systems, including vision. Consequently, artificial vision systems have become integral visual components of machines and robots. \n\nStructural engineering offers a convenient approach to enhance the flexibility and stretchability of devices. By cutting, folding, or employing both techniques, a 2D membrane device can be converted into a 3D structure while preserving its optical and electrical properties. Various structural engineering techniques, including kirigami, origami, interconnection designs, and pop-up structures, have been introduced for the fabrication of flexible and stretchable PDs. Origami structures can directly achieve hemispherical electronic eye systems (Figure 15j).345 The folding mechanism is applied to both concave and convex curvilinear arrays of photodetectors, incorporating single-crystalline silicon nanomembranes. Another research presented a photodetector array with a 3D configuration utilizing $\\mathsf{a}{\\mathsf{-}}\\mathsf{G a}_{2}\\mathsf{O}_{3}$ films on a PET substrate (Figure 15k).346 The photodetector cells reveal excellent electrical stability under large bending angle and after thousands of bending cycles. Kirigami design is based on cutting techniques other than folding prepatterned membranes. Rao et al. presented curved and shape-adaptive imagers utilizing ultrathin Si optoelectronic pixel arrays featuring a stretchable kirigami pixel design. Before stretching, the imager achieves a pixel fill factor of $78\\%$ and maintains its optoelectronic performance even when subjected to biaxial stretching of up to $30\\%$ (Figure 15l).347 In addition, islandbridge designs provide the mechanical flexibility of the device by supporting external strains during the deformation process. Sim et al. described a manufacturing technique known as conformal additive stamp (CAS) printing and demonstrate its capability to consistently produce devices with three-dimensional shapes (Figure $15\\mathrm{m}$ ). A digital camera was presented to mimic hemispherical apposition compound eyes found in biology. The high-density PDs were connected through islandbright design which shows low stress concentration in simulation results (Figure 15n). The pop-up structure has been employed in the fabrication of flexible and stretchable PDs by regulating the adhesion between material interfaces and alleviating mechanical strain through delaminated regions. The device membrane was then transferred onto the prestrained elastomeric surface (Figure 15o).350 Larger angles image detection is challenging. Cameras, inspired by the human eye, provide narrow fields of view of less than $100^{\\circ}$ . To solve this, Kim et al. presented a bioinspired camera that leverages the distinctive optical benefits of aquatic vision. This is achieved through the integration of a monocentric lens and a hemispherical silicon nanorod photodiode array. The nanorod photodiode array features a textured and passivated structure, enhancing its light sensitivity and enabling improved imaging performance, particularly under vignetting conditions (Figure 15q).351 Another design of integrated photonic systems focused on panoramic imaging. Microlens arrays featuring a flat surface and a graded refractive index profile are incorporated onto flexible comb-shaped silicon PD arrays, which are affixed to a 3D spherical structure. The combined device demonstrates an exceptionally broad field of view, encompassing nearly the entire 3D space (Figure 15p).352 \n\n \nFigure 16. Chemical sensors. (a) A gas sensor based on carbon nanotubes. (b) An electrolyte-gated graphene field-effect transistor to detect bactericidal activity. (c) $\\mathbf{MoS}_{2}$ -based field-effect transistor for NO gas detection. (d) Two-dimensional tin disulfide $\\left(\\mathsf{S n S}_{2}\\right)$ flakes-based $\\mathrm{NO}_{2}$ gas sensor. (e) $\\mathrm{Cu}_{3}\\big(\\mathrm{HHTP}\\big)_{2}$ -based FET device for gas sensing. (f,g) Metallophthalocyanine-based MOF utilized for gas sensing. $\\mathbf{\\eta}(\\mathbf{h})$ Gas sensors by integrating nanostructured metal oxides with MOF materials. (i−l) Illustration of metal oxides and their composites used for gas sensors. $\\left({\\mathrm{m-p}}\\right)$ Illustration of other materials used for gas sensors including black phosphorus, MXene, conducting polymers and quantum dots. (a) Reproduced with permission from ref 353. Copyright 2021 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. (b) Reproduced with permission from ref 354. Copyright 2010 American Chemical Society. (c) Reproduced with permission from ref 355. Copyright 2012 Wiley. (d) Reproduced with permission from ref 360. Copyright 2015 American Chemical Society. (e) Reproduced with permission from ref 356. Copyright 2018 Wiley. (f) Reproduced with permission from ref 357. Copyright 2018 American Chemical Society. (g) Reproduced with permission from ref 358. Copyright 2022 Wiley. (h) Reproduced with permission from ref 359. Copyright 2016 Wiley. (i) Reproduced with permission from ref 361. Copyright 2024 Springer Nature. (j) Reproduced with permission from ref 362. Copyright 2014 The Royal Society of Chemistry. (k) Reproduced with permission from ref 363. Copyright 2024, The Authors, Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). (l) Reproduced with permission from ref 364. Copyright 2014 American Chemical Society. (m) Reproduced with permission from ref 365. Copyright 2015 American Chemical Society. (n) Reproduced with permission from ref 366. Copyright 2017 American Chemical Society. (o) Reproduced with permission from ref 367. Copyright 2003 Springer Nature. (p) Reproduced with permission from ref 368. Copyright 2014 Wiley.",
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"category": " Results and discussion"
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"id": 11,
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"chunk": "# 2.5. Chemical Sensors \n\nChemical sensor is another cornerstone of modern soft robotic systems, facilitating their ability to perceive and interact with the surrounding environment intelligently. As soft robotics continues to advance, the integration of chemical sensors offers unprecedented opportunities for enhanced functionality and adaptability. These sensors are meticulously designed to detect and quantify specific chemical compounds or changes in the environment, providing crucial information for decisionmaking and autonomous operation. Whether deployed in medical devices for real-time diagnostics, environmental monitoring systems for pollution detection, or industrial robots for quality control, chemical sensors enable soft robots to navigate complex and dynamic environments with precision and efficacy. Harnessing the synergy between chemical sensing and soft robotics holds immense potential to revolutionize various domains, driving innovation and addressing pressing societal challenges. \n\n2.5.1. Carbon Materials. Carbon-based materials such as carbon nanotubes and graphene have been widely utilized in the fabrication of chemical sensors due to their exceptional properties. With their large surface area, excellent electrical conductivity, and chemical stability, these materials offer an ideal platform for detecting and quantifying various analytes in the environment. Additionally, their high sensitivity and selectivity make them well-suited for detecting even trace amounts of target molecules. Moreover, the inherent flexibility and tunability of carbon-based materials enable the development of flexible and wearable chemical sensors, perfectly aligned with the requirements of soft robotic applications. Sangaletti’s group investigated the efficacy of a sensor array based on functionalized carbon nanotubes (CNTs) for breath analysis, particularly in breathomics applications (Figure 16a). The study involved fabricating a sensor array comprising CNTbased sensors and assessing their ability to detect specific biomarkers in breath samples. Various gases and volatiles, such as $\\mathrm{NH}_{3},\\mathrm{NO}_{2},\\mathrm{H}_{2}\\mathrm{S},$ and benzene, were tested to evaluate the sensor array2’s s2ensitivity and selectivity.353 Loh’s group developed a bioelectronic platform leveraging a graphenelipid bilayer interface (Figure 16b). Through an electrolytegated graphene field-effect transistor, they scrutinized the electrical interaction between graphene and charged lipid membranes.354 \n\n2.5.2. Transition Metal Dichalcogenides (TMDs). The emergence of two-dimensional (2D) transition metal dichalcogenides (TMDs) has emerged as a promising avenue for the fabrication of gas sensors. Zhang’s group fabricated single- and multilayer $\\mathbf{MoS}_{2}$ films using the mechanical exfoliation method. They then used these films to fabricate field-effect transistor (FET) devices (Figure 16c). The FET devices were used to detect the adsorption of NO gas.355 Ou’s group developed a gas sensor utilizing two-dimensional tin disulfide $(\\mathrm{{SnS}}_{2})$ flakes for selective and reversible nitrogen dioxide $\\left(\\mathrm{NO}_{2}\\right)$ gas detection (Figure 16d). This sensor exhibits high sensitivity and superior selectivity to $\\mathrm{NO}_{2},$ particularly at temperatures below $160~^{\\circ}\\mathrm{C}$ . \n\n2.5.3. Metal−Organic Frameworks (MOFs). Metal− organic framework (MOF) materials have garnered significant attention in the field of chemical sensors due to their unique properties and tunability. These porous materials consist of metal ions or clusters coordinated with organic ligands, resulting in a highly porous and crystalline structure. MOFs offer a large surface area, diverse pore sizes, and adjustable chemical properties, making them ideal candidates for various sensing applications. Rubio-Giménez et al. investigated the chemiresistive response of ultrathin films composed of conductive MOFs (Figure 16e). They synthesized highly oriented semiconductive $\\mathrm{Cu}_{3}\\big(\\mathrm{HHTP}\\big)_{2}$ films using a bottomup approach and integrated them into field-effect transistor (FET)-type devices. Their study aimed to elucidate the underlying mechanisms behind the chemiresistive response observed in these devices. Combining experimental data with computational modeling, the authors proposed that changes in the electrical conductivity of the Cu-CAT-1 films are governed by the coordination of guest molecules with the open metal sites in the 2D MOF layer.356 Mirica’s group has made significant strides in the development of modular metal− organic frameworks (MOFs) utilizing metallophthalocyaninebased building blocks (Figure $16\\mathrm{f,g},$ ). These MOFs boast outstanding conductivity, low-dimensionality, high surface area, and a precisely ordered arrangement of active sites at the nanoscale. Leveraging these properties, the group has incorporated these MOFs into chemiresistive gas sensing devices, achieving excellent sensitivity and impressively low detection limits for gases like $\\mathrm{NH}_{3},$ $\\operatorname{H}_{2}S,$ and NO.357,358 Xu’s group proposed an approach to enhance the performance of chemiresistor gas sensors by integrating nanostructured metal oxides (MOXs) with MOFs into a core−sheath nanowire structure (Figure 16h). In their study, a hydrophobic and catalytic Zeolitic Imidazolate Framework-CoZn (ZIF-CoZn) thin film was coated onto $\\mathrm{znO}$ nanowires, forming a core− sheath nanowire array. This $\\mathrm{ZnO}@\\mathrm{ZIF-CoZn}$ nanowire array exhibited markedly improved sensing capabilities for acetone detection under humid conditions. It demonstrated enhanced selectivity, superior response and recovery behavior, and reduced operating temperatures compared to sensors based solely on $\\mathrm{znO}$ nanowire arrays.359 \n\n2.5.4. Metal Oxides and Composites. Fan’s group developed a highly sensitive and selective gas sensor based on biomimetic olfactory chip (BOC) technology. The fabrication process involved depositing a $\\mathrm{PdO}/\\mathrm{SnO}_{2}$ sensing film on a custom MEMS substrate using atomic layer deposition (ALD), creating a gradient film composition using sputtering, and fabricating electrodes, an insulating layer, and a heater on the substrate (Figure 16i).361 Wang’s group reported the synthesis of plate-like $\\mathtt{p-n}$ heterogeneous ${\\mathrm{NiO}}/{\\mathrm{WO}}_{3}$ nanocomposites by annealing $\\mathrm{\\Ni(OH)}_{2}$ and $\\mathrm{H}_{2}\\mathrm{WO}_{4}$ in air (Figure 16j). These nanocomposites were investigated for their gas-sensing behavior toward various toxic gases, including $\\mathrm{H}_{2}S,$ $\\mathrm{CH}_{4},$ NO, $\\mathrm{NO}_{2},$ ${\\mathrm{~\\thinspaceSO}}_{2},$ and CO.362 Liu’s group developed a hierarchical nanoheterostructure comprising HFIP-grafted $\\alpha$ - ${\\mathrm{Fe}}_{2}{\\mathrm{O}}_{3}@$ multiwall carbon nanotubes (MWCNTs) to create high-performance chemiresistive sensors for nerve agents (Figure $\\mathbf{l}6\\mathbf{k}\\overline{{\\mathbf{\\Omega}}}$ ). The synthesis process involved directly growing $\\scriptstyle\\alpha-\\mathrm{Fe}_{2}\\mathrm{O}_{3}$ nanorods onto MWCNT backbones, followed by functionalization with hexafluoroisopropanol (HFIP). These composites were utilized for detecting dimethyl methylphosphonate (DMMP), a sarin simulant gas. Results demonstrated that the HFIP- ${\\cdot\\alpha{\\cdot}\\mathrm{Fe}_{2}\\mathrm{O}_{3}}@\\mathrm{MWCNT}$ hybrids exhibited exceptional DMMP-sensing capabilities, including low operating temperature, high response, short response/recovery time, and low detection limit.363 Kim’s group reported on the development of highly sensitive and selective gas sensors for detecting $\\mathrm{H}_{2}S$ and acetone in humid environments.364 These sensors employ $\\mathrm{SnO}_{2}$ nanofibers combined with reduced graphene oxide (RGO) nanosheets to form composite sensing layers (Figure 16l). The proportion of RGO in these layers is finetuned to modify the sensors’ properties. The sensors were evaluated for their responsiveness to $\\mathrm{H}_{2}S$ and acetone at various temperatures, revealing that certain RGO concentrations yield the strongest response to each gas. They demonstrate rapid response and recovery times, and high specificity for $\\mathrm{H}_{2}S$ and acetone. \n\n2.5.5. Other Materials. Zhou’s group explored the chemical sensing capabilities of multilayer black phosphorus (BP) field-effect transistors (FETs) for detecting nitrogen dioxide $\\left(\\mathrm{NO}_{2}\\right)$ (Figure $16\\mathrm{m}\\dot{}$ ). They tested the BP FETs by exposing them to various concentrations of $\\mathrm{NO}_{2}$ and tracking the changes in conductance. These devices demonstrated a significant increase in conductance upon exposure to $\\mathrm{NO}_{2},$ showing exceptional sensitivity with detection limits as low as 5 parts per billion (ppb).365 In addition, MXene is also used for \n\n \nFigure 17. Flexible ultrasound devices with single units $\\left({\\mathsf{a}}{-}{\\mathsf{d}}\\right)$ or array devices (d−k). (a) Schematic of single-unit-based ultrasound device. (b) Ultrasonic autopositioning for soft robotic perception system. Reproduced with permission from ref 370. Copyright 2023 American Chemical Society. (c) Miniaturized electromechanical devices for deep tissue characterization. Reproduced with permission from ref 371. Copyright 2021 Springer Nature. (d) Mechano-acoustic sensing of physiological process and body motions. Reproduced with permission from ref 372. Copyright 2020 Springer Nature. (e) Schematic of array-based ultrasound device. (f) Stretchable ultrasonic phased arrays for continuous monitoring of deeptissue hemodynamics. Reproduced with permission from ref 373. Copyright 2021 Springer Nature. (g) Bioadhesive ultrasound for long-term \n\ncontinuous imaging of diverse organs. Reproduced with permission from ref 374. Copyright 2022 American Association for the Advancement of Science. (h) Stretchable ultrasonic transducer arrays for imaging on complex surfaces. Reproduced with permission from ref 375. Copyright 2018 American Association for the Advancement of Science. (i) A conformal ultrasonic device for monitoring central blood pressure waveform. Reproduced with permission from ref 376. Copyright 2018 Springer Nature. (j) A wearable cardiac ultrasound imager. Reproduced with permission from ref 377. Copyright 2023 Springer Nature. $(\\bar{\\mathbf{k}})$ A conformal phased-array ultrasound patch for bladder volume monitoring. Reproduced with permission from ref 378. Copyright 2024 Springer Nature. \n\ngas sensor fabrication. Kim’s group has developed wearable gas sensors using $\\mathrm{Ti}_{3}\\mathrm{C}_{2}\\mathrm{T}_{\\boldsymbol{x}}$ nanosheets, a type of two-dimensional material derived from $\\mathrm{Ti}_{3}\\mathrm{AlC}_{2}$ (Figure 16n). These nanosheets are integrated onto flexible polyimide platforms, creating sensors that can operate at room temperature. The sensors’ performance was evaluated against various gases including ethanol, methanol, acetone, and ammonia. The results demonstrated a p-type sensing behavior, with the highest sensitivity to ammonia and the least to acetone, where the detection limit for acetone was theoretically established at about 9.27 ppm compounds, underscoring the diagnostic potential of this technology.366 Janata et al. explored the application of conducting polymers in electronics, particularly in the construction of devices and as selective layers in chemical sensors. The study emphasizes the conductivity and work function of conducting polymers and investigates how these properties are influenced by interactions with ambient gases (Figure 16o). This interaction is critical for understanding the functionality of conducting polymers in various environmental conditions. Additionally, the authors describe various electronic devices that can be fabricated using conducting polymers, including chemiresistors, field-effect transistors (FETs), capacitors, and diodes.367 Tang’s group conducted research on the sensing mechanism of $\\mathrm{NO}_{2}$ gas using lead sulfide colloidal quantum dots (PbS CQDs) (Figure $16\\mathrm{p})$ . They synthesized these quantum dots and used them to develop a high-performance, paper-based flexible $\\mathrm{NO}_{2}$ gas sensor. The study explored various treatments to enhance the sensing characteristics of the devices. It was found that $\\mathrm{NO}_{2}$ interacts with the surface of the PbS CQDs, leading to p-type doping and a decrease in resistance. These sensors not only operate effectively at room temperature but also demonstrate high sensitivity and rapid response to $\\mathrm{NO}_{2}$ exposure. Additionally, the flexibility and durability of the paper-based sensors highlight their potential for practical applications in environmental monitoring and safety.368",
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"category": " Results and discussion"
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},
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{
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"chunk": "# 2.6. Acoustic Sensors \n\nAcoustic devices encompass a diverse array of technologies that harnessing sound waves to fulfill a multitude of functions, spanning from communication and sensing to medical imaging and industrial testing.369 Acoustic waves are mechanical vibrations that propagate through mediums like air, water, or solid materials, characterized by frequencies, wavelengths, and amplitudes. These waves can be broadly classified into three main types based on their frequencies: audible waves $20\\mathrm{Hz}$ to $20,000~\\mathrm{Hz},$ , ultrasound waves $\\left(>20,000~\\mathrm{Hz}\\right)$ , and infrasonic waves $\\left(<20~\\mathrm{Hz}\\right)$ . Given their crucial applications in fields like healthcare and imaging, our focus here will primarily center on ultrasound and audible ranges. \n\nThe audible range pertains to frequencies detectable by the human ear, where sound waves manifest as various pitches. Lower frequencies correspond to deeper tones, like those of a bass drum, while higher frequencies produce sharper sounds, like to a whistle. In contrast, ultrasound waves possess shorter wavelengths and higher frequencies than audible sound waves, enabling them to penetrate materials and generate detailed images of internal structures. Ultrasound technology finds extensive use not only in medical imaging, including prenatal ultrasounds for monitoring fetal development, but also in industrial applications, such as nondestructive material testing. Thereby, acoustic devices serve as indispensable tools across a spectrum of fields, facilitating communication, sensing, imaging, and measurement across frequencies ranging from the audible realm to ultrasound frequencies. Their adaptability and efficacy underscore their pivotal role in modern technology and everyday life. \n\n2.6.1. Ultrasound Waves. Ultrasound devices have revolutionized various fields, including medical imaging, longterm healthcare monitoring, and intelligent robotics. This is because ultrasound waves have the ability to penetrate not only materials but also the human body without harmful effects, allowing them to provide detailed information about internal organs. Based on the number of transducers, these devices can be categorized into two types: single-unit-based ultrasound devices and array-based ultrasound devices. \n\nSingle-unit ultrasound devices typically consist of a single transducer element that emits and receives ultrasound waves (Figure 17a). Based on the information obtained from these waves, useful data can be analyzed and extracted. These devices are simple and compact, making them suitable for portable applications and point-of-care diagnostics. In robotic systems, ultrasonic sensors have been integrated with flexible triboelectric sensors to achieve not only remote object positioning but also multimodal cognitive intelligence. Such advancements are pivotal in expanding the capabilities of soft robotic systems, paving the way for industrial automation and healthcare (Figure 17b).370 For the diagnosis of human body conditions, miniaturized electromagnetic devices have been designed to measure the Young’s modulus of skin and soft tissue, offering a novel approach to accurately locating lesions associated with skin conditions like psoriasis. This facilitates early intervention and personalized treatment strategies (Figure 17c).371 Moreover, these devices enable monitoring of various physiological processes for healthcare and rehabilitation. For instance, skin-mounted soft electronics incorporating triaxial accelerometers enable continuous measurement of mechano-acoustic signals, including heart rate, respiration, and subtle body movements (Figure 17d).372 \n\nOn the other hand, array-based ultrasound devices consist of multiple transducer elements arranged in a linear or twodimensional array (Figure 17e). By controlling the timing and amplitude of each transducer element, array-based devices can produce focused ultrasound beams and perform advanced imaging techniques such as beamforming and synthetic aperture imaging. This allows for improved image quality, spatial resolution, and visualization of complex structures in three dimensions. For example, a skin-conformal ultrasonic phased array capable of monitoring hemodynamic signals from deep tissues, empowering individuals to track their cardiovascular health in real-time and facilitating early intervention in case of abnormalities (Figure 17f).373 Similarly, a bioadhesive ultrasound device for continuous imaging of internal organs over extended periods. The ability, providing uninterrupted imaging of diverse internal structures, holds immense potential for diagnosing and monitoring many medical conditions, from gastrointestinal disorders to cardiac abnormalities (Figure 17g).374 \n\n \nFigure 18. Flexible acoustic devices with different mechanisms. (a) Transparent and conductive nanomembranes for skin-attachable loudspeakers and microphones. Reproduced with permission from ref 379. Copyright 2018 American Association for the Advancement of Science. (b) An acoustic interface for wearable human−machine interaction. Reproduced with permission from ref 380. Copyright 2021 Wiley. (c) Acoustic smart skin for human−machine interface. Reproduced with permission from ref 381. Copyright 2022 American Association for the Advancement of Science. (d) An ultrathin conformable vibration-responsive electronic skin for vocal recognition. Reproduced with permission from ref 382. Copyright 2019 Springer Nature. (e) An ultrasensitive artificial mechanotransducer skin. Reproduced with permission from ref 383. Copyright 2017 Wiley. (f) Acoustic fabrics via single fiber. Reproduced with permission from ref 384. Copyright 2022 Springer Nature. (g) Fully flexible electromagnetic vibration sensor with origami magnetic membranes. Reproduced with permission from ref 385. Copyright 2020 Wiley. (h) An ultrasensitive MXene-based intelligent artificial eardrum. Reproduced with permission from ref 386. Copyright 2022 American Association for the Advancement of Science. \n\nThe development of a stretchable ultrasound probe marks another milestone in ultrasound technology (Figure 17h).375 This innovative probe can conform to nonplanar complex surfaces, enabling high-resolution imaging through anatomically challenging regions. Such advancements are invaluable in fields like structural engineering, where detecting defects in intricate geometries is paramount. Furthermore, wearable ultrasonic devices, designed for continuous monitoring of central blood pressure waveforms (Figure 17i), continuous cardiac function assessment (Figure 17j) and volumetric organ monitoring (Figure 17k), represent significant strides toward personalized healthcare.376−378 By leveraging advancements in materials science and signal processing algorithms, these devices offer unparalleled accuracy and reliability, empowering individuals to monitor their health proactively. \n\n \nFigure 19. Flexible magnetoreception and Electroreception sensing systems for soft robotics. (a) Flying migrating birds. Credits: Jiangtao Su (b) Pigeons’ organ for navigation. Reproduced with permission from ref 387. Copyright 2003 Wiley. (c) The mechanism of navigation organ. Reproduced with permission from ref 388. Copyright 2012 American Association for the Advancement of Science. Flexible magnetoreception based on different mechanisms: (d) Hall effect-based flexible tactile sensors. Reproduced with permission from ref 389. Copyright 2021 Springer Nature. (e) Magneto-piezoresistance based touchless Sensing Interface. Reproduced with permission from ref 390. Copyright 2021 American Chemical Society. (f) GMR based e-skins. Reproduced with permission from ref 391. Copyright 2020 American Association for the Advancement of Science. (g) AMR-based flexible magnetic sensors. Reproduced with permission from ref 392. Copyright 2016 Wiley. (h) Electrosensory systems in sharks. Reproduced with permission from ref 393. Copyright 2022 American Association for the Advancement of Science. Electroreception systems based on different mechanisms: (i) Electrostatics based electroreceptors for precontact somatosensation. Reproduced with permission from ref 393. Copyright 2022 American Association for the Advancement of Science. (j) Flexible capacitive sensor for wireless perception. Reproduced with permission from ref 394. Copyright 2021 IEEE. (k) Flexible capacitive sensor sheet for proximity detection. Reproduced with permission from ref 395. Copyright 2021 Wiley. \n\nIn summary, recent advancements in ultrasound technology have propelled the field toward unprecedented heights. Whether in the form of single-unit devices or array-based systems, these innovations hold the potential to revolutionize diverse industries and improve the quality of life for millions worldwide. \n\n2.6.2. Audible Waves. Acoustic devices for audible waves play a crucial role in various applications, from human machine interaction to healthcare monitoring. This part explores recent developments in acoustic sensors across different mechanisms, including triboelectric, capacitive, piezoelectric, magnetic, and resistive sensors, highlighting their unique capabilities and potential applications. \n\nTriboelectric sensors harness the principle of triboelectricity, where contact electrification occurs due to the contact and separation of materials. For example, the development of ultrathin and transparent hybrid nanomembrane, embedding with silver nanowire arrays within a polymer matrix (Figure 18a), enables the creation of skin-attachable loudspeakers and microphones, facilitating voice recognition and human− machine interaction.379 Another example is the waterproof \n\nAcoustic Sensor designed to serve as a wearable translation interface for human−machine interaction, covering almost the entire human audible range from 0.1 to $20\\mathrm{kHz}$ . It functions as a high-fidelity auditory platform suitable for various applications, including music recording and speech recognition (Figure 18b).380 Furthermore, dual-mode human−machine interfaces have also been explored, such as frequency-selective acoustic and haptic sensors with hierarchical structures showing high sensitivity and noise-independent voice recognition (Figure 18c).381 \n\nCapacitive sensors utilize changes in capacitance to detect acoustic signals, typically by measuring the distance change between electrodes. For example, ultrathin, conformable vibration-responsive electronic skins have been developed capable of detecting skin acceleration correlated with voice pressure (Figure 18d).382 These sensors exhibit outstanding sensitivity and skin conformity, making them suitable for voice recognition applications such as security authentication and remote-control systems. Additionally, capacitive sensors with waterproof capability have been developed, serving as wearable translation interfaces for human−machine interaction (Figure 18e).383 These sensors enable users to interact with devices in various environments without concern for water damage, expanding their potential applications in both consumer electronics and industrial settings. \n\nPiezoelectric sensors convert mechanical stress into electrical signals, offering high sensitivity and frequency response. Fabrics integrated with piezoelectric fibers have been developed, functioning as sensitive audible microphones while retaining fabric qualities such as washability and draping (Figure 18f).384 These fabrics enable applications ranging from precise acoustic impulse direction measurement to cardiac sound auscultation, opening up new possibilities for smart clothing. Magnetic sensors detect mechanical motion through changes in magnetic fields. For example, the development of fully flexible electromechanical system sensors for wearable monitoring of mechanical displacement (Figure $18\\mathrm{g}$ ).385 These sensors offer broad frequency responses and high sensitivities, enabling applications in biophysical sensing, motion detection, and voice recognition. Resistive sensors, such as those based on two-dimensional MXene materials, mimic the function of human eardrums for voice detection and recognition (Figure 18h).386 MXene-based artificial eardrums exhibit extremely high sensitivity and low detection limits, enabling accurate voice classification through machine learning algorithms. These sensors hold promise for wearable acoustical healthcare devices.",
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"category": " Results and discussion"
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},
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{
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"id": 13,
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"chunk": "# 2.7. Electromagnetic Sensors \n\nElectromagnetic sensing is crucial in robotics due to its ability to gather information about the surrounding environment using electromagnetic waves. These waves encompass various frequencies, such as radio waves, microwaves, and infrared radiation, allowing robots to perceive their surroundings in diverse conditions and across different distances. By detecting electromagnetic signals reflected off objects or emitted by them, robots can accurately sense distances, identify obstacles, and navigate through complex environments. Overall, electromagnetic sensing plays a pivotal role in robotics by providing vital sensory input, enhancing robot perception, and facilitating intelligent decision-making processes, ultimately contributing to their autonomy and effectiveness in a wide range of applications. Here, we focused on the magnetoreception and electroreception systems. \n\n2.7.1. Magnetoreception. Migrating birds are capable navigators, relying on a combination of sensory cues and innate abilities to orient themselves and navigate across varying distances (Figure 19a). They can use the Earth’s magnetic field for orientation, adjusting their flight paths accordingly. Fleissner et al. examined the subcellular organization of afferent trigeminal terminals in the upper beak of the homing pigeon, Columba livia (Figure 19b).387 These terminals are approximately $5~\\mu\\mathrm{{m}}$ in diameter and contain superparamagnetic magnetite (SPM) crystals. The surroundings of one SPM cluster are the subcellular structure of a putative pigeon-type magnetoreceptor. The current model of avian magnetoreception proposes the existence of two magnetic sensory structures: one located in the eye, which provides a magnetic reference direction, and another situated in the upper beak to gauge magnetic-field intensity, serving as input for the navigational map. Neurons within the pigeon brain encode information regarding Earth’s magnetic field to facilitate orientation and navigation (Figure 19c).388 \n\nInspired from navigable birds, robotics with the magnetosensing ability shows great potential to obtain an inherently touchless detection, opening up a broad spectrum of interaction scenarios.396 Magnetoreceptors’s mechanisms can be roughly classified into four types: Hall effects, magnetopiezoresistance, giant magnetoresistive (GMR) and anisotropic magnetoresistive (AMR).397 Kaidarova et al. reported a type of laser-scribed graphene Hall sensors with linear response to magnetic fields. They also exhibit a low constant noise voltage floor for a bias current of $100\\mu\\mathrm{A}$ at room temperature, which is comparable with state-of-the-art low-noise Hall sensors (Figure 19d).389 In another work, Zhang et al. proposed a new touchless sensing device based on the magneto-piezoresistive effect (Figure 19e).390 By having a hierarchical magnetoelastomer coated with a 3D piezoresistive network for touchless tactile perception, the attractive magnetic force induced by the approaching magnetic material initially acts on the ferromagnetic substrate and then transmits to the coated piezoresistive layer, leading to a change in resistance. GMR is a quantum mechanical magnetoresistance effect seen in multilayers made of alternating ferromagnetic and nonmagnetic conducting layers. Kondo et al. reported the development and low-voltage operation of an imperceptible organic electronic system comprising solely p-type organic thin-film transistors (OTFTs), capable of integrating all components required for the operation of an active magnetosensory matrix (MSM) system (Figure 19f).391 Unlike GMR, the AMR effect is a phenomenon that the resistance of anisotropic magnetic materials changes with the angle between the magnetization and the current direction. A self-biased AMR magnetic field sensors was proposed, which exhibit a sensitivity limit of approximately $150~\\mathrm{nT}$ at $3\\:\\mathrm{Hz}$ when fabricated on PR-buffered flexible PET foils (Figure $19\\mathrm{g}$ ).392 These sensors also demonstrate a sensitivity of $42~\\Bar{\\mathrm{T}}^{-1}$ , similar to that of AMR sensors on rigid oxidized silicon substrates. Furthermore, the AMR sensors on flexible substrates exhibit excellent deformation stability, with no degradation in electrical output observed even at bending radii as small as $5~\\mathrm{mm}$ . \n\n2.7.2. Electroreception. Interestingly, certain organisms in nature, such as sharks inhabiting the dark sea, utilize an electroreception strategy for remote perception. Sharks possess the ability to detect minute electric field gradients in the ambient environment through a multitude of electroreceptors dispersed across their head. Electric signals are captured by dermal pores and conveyed to the shark’s electrosensory cells. \n\n \nFigure 20. Flexible sensors for normal and shear force detection based on $\\left(\\mathsf{a}-\\mathsf{f}\\right)$ piezoresistive, (i−l) capacitive, and $\\left({\\mathrm{m-p}}\\right)$ magnetic mechanism. (a) Photograph of the mechanoreceptor-inspired soft sensing device with multimodal tactile information detection abilities. Reproduced with permission from ref 400. Copyright 2022 Wiley. (b) Flexible sensor that can detect the direction of external force based on self-adjusting CNT arrays. Reproduced with permission from ref 401. Copyright 2018 IOP Publishing. (c) Schematic illustration of the structure of tenon and mortiseinspired six-dimension force sensor. Reproduced with permission from ref 402. Copyright 2022 Elsevier. (d) Top: Schematic illustration of the flexible sensor with opposite resistance responding that can detect normal and tangential forces. Bottom: photograph of the flexible force sensor. Reproduced with permission from ref 403. Copyright 2018 Wiley. (e) Schematic illustration of the skin-inspired multimodal mechanoreceptor that can detect normal and shear forces, and hardness, texture, and tackiness of objects. Reproduced with permission from ref 404. Copyright 2024 Wiley. (f) Photograph of the three-dimensional piezoresistive structures with multimodal sensing abilities. Reproduced with permission from ref 405. Copyright 2019 American Association for the Advancement of Science. (g) Structure of the three-axial flexible capacitive force sensor. Reproduced with permission from ref 406. Copyright 2011 IOP Publishing. (h) Flexible three-axial capacitive tactile sensor with multilayered dielectric structures. Reproduced with permission from ref 407. Copyright 2017 Springer Nature. (i) Photograph of the highly sensitive flexible three-axial force sensor. Reproduced with permission from ref 408. Copyright 2014 Wiley. (j) Schematic illustration of the pyramid-plug structureinspired tactile sensor for measurement of pressure, shear force, and torsion. Reproduced with permission from ref 409. Copyright 2018 Wiley. (k) Photograph of the polymer-based capacitive sensor array that can measure normal and shear forces. Reproduced with permission from ref 410. Copyright 2010 MDPI. (l) Hierarchically patterned e-skin that can detect the direction of forces and its integration on a robotic hand. Reproduced with permission from ref 234. Copyright 2018 American Association for the Advancement of Science. $\\mathrm{(m)}$ Structure of the three-axial Hall effectbased tactile sensor. Reproduced with permission from ref 411. Copyright 2016 MDPI. (n) Photograph of the split-type magnetic tactile sensors. Reproduced with permission from ref 412. Copyright 2023 Wiley. (o) Soft magnetic e-skin with high tactile sensing resolution and force decoupling capabilities. Reproduced with permission from ref 269. Copyright 2021 American Association for the Advancement of Science. (p) Schematic illustration of the multimodal magnetoelastic e-skin that can work underwater. Reproduced with permission from ref 413. Copyright 2024 American Association for the Advancement of Science. \n\nSubsequently, electrically gated ion channels are activated, facilitating the movement of ions across the cell membrane (as shown in Figure 19h).393 Based on electrostatic induction effect, an artificial electroreceptor was designed for detecting approaching targets. (Figure 19i).393 This receptor can encode environmental precontact information into a sequence of voltage pulses, serving as distinctive interfaces for precontact interactions with humans. Flexible capacitive sensors are also used for electroreception in distant-response scenarios. Qin et al. introduced a flexible dual-mode capacitive sensor for interactive and wireless sensing of a proximity contact interface (Figure 19j).394 The sensors were fabricated via large-scale screen printing and rapid laser engraving techniques. Incorporating a microcone structure on a dielectric film, the dual-mode sensors achieved a detection distance of up to 20 cm in proximity mode, with an expanded operating range of $200\\mathrm{kPa}$ , a low detection limit of $10.2\\mathrm{Pa},$ , and a rapid response time of $90~\\mathrm{{ms}}$ . In a flexible trimodal sensing systems, proximity sensing sensor sheet relies on self-capacitance measurement (Figure 19k).395 An electrode layer functions as a sensing electrode, emitting electric field (E-field) lines in multiple directions when positively charged. As a conductor (e.g., a hand) approaches, whether grounded or capacitively coupled to virtual ground, E-field lines partially converge toward its surface, resulting in increased capacitance between the sensing electrode and ground.",
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"category": " Results and discussion"
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"chunk": "# 2.8. Multimodal Integration \n\nThe aforementioned sections have discussed sensing technologies primarily designed to detect singular stimuli. However, simultaneous detection of multiple stimuli is crucial for obtaining a comprehensive understanding of the surrounding environment.398 For instance, in robotic systems, acquiring both pressure and temperature information is essential for successfully handling a cup of hot water and determining its temperature. This cannot be accomplished with sensors utilizing a single sensing modality. This section focuses on introducing multimodal sensing technologies integrated into soft robots, which represent a pivotal step toward enabling real-world applications of robotics in various domains. \n\n2.8.1. Normal and Shear Force. To date, there has been relatively fewer reports on flexible sensors capable of measuring both normal and shear forces compared to sensors solely designed for normal force detection. This discrepancy arises from the inherent complexity involved in their design, fabrication, integration, and data acquisition.399 Nonetheless, the necessity of measuring both normal and shear forces is undeniable in robotic applications, particularly for tasks involving dexterous manipulation, slip detection, and advanced manufacturing processes. Designing sensors for such multifaceted measurements typically relies on employing appropriate structural and geometric configurations. In our overview, we categorize these sensors based on three primary working mechanisms: resistive, capacitive, and magnetic. \n\nBy mimicking the working principles of mechanoreceptors in human skin, Gao et al. reported a simple, thin, soft, intelligent tactile sensor that can detect normal and shear forces, which is composed of an asymmetrically arrangement of strain sensing unit array, as shown in Figure 20a.400 The sensitivity for such force detection can be reached to $0.11~\\mathrm{~kPa}^{-1}$ , $2.5~\\ \\mathrm{N}^{-1}$ , respectively. \n\nLee et al. also presented a highly sensitive force sensor leveraging self-adjusting carbon nanotube (CNT) arrays (Figure 20b).401 These arrays are grown directly on silicon microstructures using a space-confined growth technique, facilitating effortless self-adjustment upon contact and the microstructures with integrated CNTs are embedded in polydimethylsiloxane structures to further enhance the flexibility and softness. The sensing principle for this device relies on detecting variations in contact resistance between opposing CNT arrays when force from different direction is applied. Inspired by tenon-and-mortise in traditional Chinese ancient architecture, Hu et al. designed a flexible sixdimensional force sensor with interlocking structures (Figure 20c), providing a new strategy for the design of multidimensional force sensors.402 As shown in Figure 20d, another versatile tactile sensor capable of detecting both normal and tangential forces was reported by Mu et al.403 This flexible and stretchable e-skin features a two-layered structure comprising carbon nanotubes (CNTs) and graphene oxide (GO) integrated into a 3D conductive network anchored on a thin porous polydimethylsiloxane (PDMS) layer. Inspired by the structure of skin on fingertips and the active sensing strategies of biological species, Su et al. reported an artificial 3D mechanoreceptor (SENS) capable of detecting multiple mechanical stimuli, such as normal and shear force, as shown in Figure 20e.404 Additionally, a tensor-based nonlinear theoretical model was developed to characterize the threedimensional deformation (including tensile, compressive, and shear deformation) of the SENS, offering valuable insights for designing and optimizing its multimodal sensing properties with high accuracy. Furthermore, this work introduced a comprehensive functional framework establishing a link between sensing and action within the closed-loop sensorimotor control of robots engaged in dynamic haptic exploration. This framework enables the robotic system integrated with SENS to autonomously detect attributes such as hardness, surface texture, and tackiness. By employment of three-dimensional structure in the design, Won et al. presented a microelectromechanical sensor with monocrystalline silicon nanomembranes as piezoresistive elements (Figure 20f).405 This design facilitates independent and concurrent measurements of various mechanical stimuli, including normal force, shear force, bending, and temperature. Moreover, the fabrication and assembly procedures enable scalable manufacturing of interconnected arrays of these sensors, facilitating spatiotemporal mapping capabilities. \n\nApart from resistive sensors, sensors based on capacitive principles can also be designed for spatial force measurement. Lee et al. reported real-time measurement of diverse contact forces applied to a novel flexible capacitive three-axis tactile sensor array (Figure $20\\mathrm{g}$ ), which is constructed using polydimethylsiloxane (PDMS).406 To dissect contact forces into their normal and shear components, each unit sensor is equipped with four capacitors, strategically separated by walltype spacers to enhance mechanical response time. Unlike traditional dielectric layers comprising a single material, Huang et al. incorporated a multilayered dielectric consisting of both air gaps and polydimethylsiloxane for a flexible three-axial force sensor (Figure 20h).407 The structural changes in the multilayered dielectric under external forces result in variations in dielectric constant $(\\varepsilon)$ , leading to capacitance alterations. Measurement outcomes demonstrate a detectable force range of approximately $_{0-10\\mathrm{~N~}}$ for all three axes. \n\nAlternatively, by leveraging conductive fabrics, Viry et al. introduced a compact, three-dimensional flexible capacitive three-axial force sensor (shown in Figure 20i), demonstrating superior performance through innovative dielectric multilayer structuring and material combinations.408 The sensor exhibits exceptional compliance, robustness, and stability during handling, along with remarkable sensitivity (less than $10~\\mathrm{mg}$ and $8\\mu\\mathrm{m}$ minimal detectable weight and displacement, respectively) and a wide detection range (measured up to $190\\ensuremath{\\mathrm{~\\kPa~}}$ , estimated up to $400~\\mathrm{kPa},$ . Choi et al. further developed a pyramid-patterned ionic gel inspired by neural mechanoreceptors and engraved electrodes, as shown in Figure 20j.409 Leveraging the pyramid-plug structure, the sensor’s deformation mechanism varies depending on the type of external mechanical loading applied. Notably, the sensor exhibits high sensitivities of $1.93~\\mathrm{kPa}^{-1}$ , $29.88\\ \\mathrm{\\dot{N}^{-1}}$ , and 3.39 $\\mathrm{(N~cm)^{-1}}$ for pressure, shear force, and torsion. Furthermore, the sensor offers versatility by functioning through either capacitive or piezoresistive transduction methods. While the examples introduced above mainly focus on single three-axial force sensor units, it is worth noting that sensor arrays based on similar capacitive mechanisms can also be developed, as shown in Figure $20\\mathrm{k}^{410}$ and Figure 20l.234 Cheng et al. introduced a polymer-based capacitive sensing array capable of measuring both normal and shear forces, achieved through micromachining techniques and flexible printed circuit board (FPCB) technologies. Each shear sensing element comprises four capacitive sensing cells arranged in a $2\\times2$ array, with each cell featuring two sensing electrodes and a common floating electrode, which could simplify the capacitive structures, and enhance manufacturability. Boutry et al. presented a biomimetic soft e-skin enabled by a threedimensional structure mimicking the interlocked dermisepidermis interface in human skin. The e-skin features pyramid microstructures arranged along nature-inspired phyllotaxis spirals to enhance the performance, and comprises a capacitor array capable of real-time measurement and discrimination of both normal and tangential forces. The e-skin demonstrates its utility by providing sensing feedback for controlling a robot arm across various tasks, showcasing its potential applications in robotics with tactile feedback. \n\n \nFigure 21. Flexible sensors integrated with multimodal sensing capabilities. (a) Ion-based e-skin with strain-temperature decoupling capabilities. Reproduced with permission from ref 416. Copyright 2020 American Association for the Advancement of Science. (b) Tactile-olfactory sensing array inspired by star nose. Reproduced with permission from ref 417. Copyright 2022 Springer Nature. (c) Wireless platform that can detect vascular pressure, flow rate and temperature. Reproduced with permission from ref 418. Copyright 2023 Springer Nature. (d) Photograph of soft gripper integrated with multimodal sensors that can measure proximity and temperature. Reproduced with permission from ref 419. Copyright 2022 Springer Nature. (e) Structure of the artificial sensory neuron with visual-haptic fusion. Reproduced with permission from ref 420. Copyright 2020 Springer Nature. (f) Photograph of multifunctional flexible sensing skin integrated on a curved surface. Reproduced with permission from ref 421. Copyright 2021 Elsevier. (g) Soft robotic manipulator integrated with self-powered sensors for pressure, strain, and temperature perception. Reproduced with permission from ref 422. Copyright 2021 Wiley. (h) Schematic structure of the ferroelectric skin inspired by fingertip skin that can detect temperature, static and dynamic pressure. Reproduced with permission from ref 423. Copyright 2015 American Association for the Advancement of Science. (i) Schematic illustration of the nonvon Neumann architecture for multiple signal processing. Reproduced with permission from ref 424. Copyright 2022 American Association for the Advancement of Science. \n\nTactile sensors utilizing magnetic mechanisms offer an alternative solution for three-axial force sensing. Typically, these sensors incorporate small magnets embedded in a polymer matrix or magnetic composites, beneath which resides a Hall sensor for measuring magnetic information (Figure $20\\mathrm{m},$ ).411 When subjected to external forces, the deformation of elastomeric materials occurs, leading to variations in magnetic flux densities within the materials. This change can be accurately detected by the Hall sensor. As shown in Figure $20\\mathbf{n}_{\\cdot}$ , Dai et al. reported a split-type magnetic soft tactile sensor inspired by the layered structures found in tactile sensory organs like human skin and fish lateral lines.412 By employing a centripetal magnetization arrangement and theoretical decoupling model, the sensor achieves wireless 3D force sensing with high accuracy $(1.33\\%)$ . Its 3D force decoupling capability enables perception akin to human skin in multiple dimensions without requiring complex calibration. Yan et al. also introduced a soft tactile sensor with self-decoupling and super-resolution capabilities (Figure 20o), achieved by designing a sinusoidally magnetized flexible film (∼0.5 mm thick).269 The sensor accurately measures normal and shear forces (in one dimension) with a single unit, achieving a 60-fold superresolved accuracy through deep learning. While the devices mentioned above typically operate in ambient environments, their future applications may extend beyond these settings. Particularly, the demand for flexible devices functioning in extreme conditions is increasing for applications such as outer space and deep-sea exploration. By using giant magnetoelasticity in soft polymer systems, Zhou et al. reported a multimodal underwater robotic skin (Figure $20\\mathrm{p}$ ),413 which provide a solution for intelligent machines in extreme environment. Besides the above-mentioned mechanism, there are also 3-axis flexible sensors based on other approach, such as optical and piezoelectric.414,415 \n\n2.8.2. Other Integrated Multimodal Sensing Technologies. Multimodal sensing refers to the integration of multiple sensing capabilities within a single flexible or stretchable platform. Such capabilities are enabled by employing different types of sensors for various kinds of physical, chemical, or biological information detection. For example, a flexible artificial multimodal ionic receptor capable of distinguishing between thermal and mechanical stimuli was introduced by You et al.416 They found that two variables: charge relaxation time and the normalized capacitance in this device can serve as a strain-insensitive intrinsic measure of absolute temperature, and temperature-insensitive extrinsic measure of strain, respectively. This artificial receptor can concurrently detect temperature and strain by assessing these variables at only two measurement frequencies without signal interference, providing real-time information on force direction and strain profiles during various tactile motions such as shear, pinch, spread, and torsion (Figure 21a). Inspired by the natural sense-fusion mechanism observed in the star-nosed mole, Liu et al. developed a tactile-olfactory sensing array inspired by the natural sense-fusion mechanism observed in the star-nosed mole, as shown in Figure 21b.417 Without relying on visual input, this array enables real-time capture of local topography, stiffness, and odor from a variety of objects and can achieve an accuracy of $96.9\\%$ for classification of 11 typical objects. This tactile-olfactory bionic sensing system demonstrated remarkable resilience to environmental interference, underscoring its potential for robust object recognition in difficult environments where conventional methods may falter. \n\nRecently, Kwon et al. presented the design features and operational characteristics of an integrated wireless multimodal sensor, which was specifically engineered for implantation within the heart or a blood vessel. This sensor enables simultaneous real-time measurements of pressure, flow rate, and temperature (Figure 21c).418 Moreover, there are also examples of flexible multimodal sensors integrated on soft robots, morphing aircraft, and other objects. Ham et al. introduced a versatile multimodal sensor network for proximity and temperature detection (Figure 21d).419 This sensor network was seamlessly integrated into a flexible and stretchable soft robotic hand for food applications and interacting with a warm baby doll for medical purposes. Apart from pressure, temperature, and olfaction, vision also plays a crucial role in the perception of biological species. Wan et al. developed a bimodal artificial sensory neuron to replicate sensory fusion processes in human beings.420 Optical and pressure information were collected by the neuron first, followed by transmitting into postsynaptic currents, as shown in Figure 21e. Such fusion of visual and tactile signals was conformed to enhance the recognition capability by simulating a multitransparency pattern recognition task. Xiong et al. further demonstrated advancements of aircrafts enabled by multifunctional e-skin that have capabilities to accurately measure surface pressure, temperature, wall shear stress, and flutter, as well as detect sudden impacts and predict separation and stall occurrences (Figure 21f).421 As illustrated in Figure $21\\mathrm{g},$ Sun et al. also demonstrated that soft robotic grippers integrated with self-power multifunctional sensor can automatically recognize 28 objects with an accuracy of 97.143%.422 In this system, pressure information was acquired from triboelectric nanogenerators, and temperature information was from pyroelectric temperature sensors. It is interesting to note that human skins are not only sensitive to static pressure, but also to dynamic pressure such as vibrations with different frequencies. On the one hand, this is attribute to the four types of mechanoreceptors embedded in the skin, in which two of them are slow adapting receptors and another two are fasting adapting receptors. On the other, the distinctive fingerprint patterns and intricately interlocked epidermal−dermal microridges serve a crucial function in amplifying and transmitting tactile signals to diverse mechanoreceptors, facilitating the spatiotemporal perception of both static and dynamic tactile stimuli. Drawing inspiration from this structure, Park et al. proposed fingerprint-like patterns and interlocked microstructures within ferroelectric films, as shown in Figure 21h, which enable superior piezoelectric, pyroelectric, and piezoresistive sensing of both static and dynamic mechanothermal signals.423 This artificial skin exhibits the capability to detect and differentiate between multiple spatiotemporal tactile stimuli, including static and dynamic pressure, vibration, and temperature, with remarkable sensitivity. Besides the aforementioned examples of multimodal sensing, there are still many other designs, enabling technologies, and combinations of sensing modalities. This will be remained as a research frontier for both scientists and engineers. Once the number and type of sensor increase, data acquisition and processing would become problematic. To solve this issue, Ho et al. introduced an artificial synaptic multiplexing unit capable of facilitating a parallel multi-input control system (Figure 21i).424 This innovative multi-input control system can simultaneously handle input and feedback signals, representing a significant advancement for industries reliant on processing large volumes of streaming data.",
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"chunk": "# 2.9. Future Development \n\nIn short conclusion, this section delves into the various mechanisms, materials, and applications of these sensing technologies to provide a thorough understanding of the current state and future potential of sensor systems. This knowledge is essential for researchers, engineers, and practitioners seeking to develop and deploy advanced sensing solutions in an increasingly interconnected and data-driven world. However, there are endless frontiers for the exploration of soft sensors in the next few decades, as exemplified by recent work on flexible sensors.425−427 Here, we summarized the three main aspects for the future development of soft sensors and also provided our opinions for corresponding issues: mechanism selection, metrics optimization, and system integration (Figure 22). \n\nSelecting an appropriate sensing mechanism requires a comprehensive understanding of the strengths and limitations of each option. The selection process should align with the specific requirements and performance objectives of the intended application. For example, resistive sensors are valued for their low cost and simplicity, making them suitable for integration (Figure 22a). However, they exhibit limitations such as hysteresis, which compromises accuracy during cyclic measurements, susceptibility to drift over time, and sensitivity to temperature fluctuations, restricting their reliability in dynamic environments. Capacitive sensors, characterized by high sensitivity due to their ability to convert external stimuli into structural deformations, are ideal for high-precision applications like touchscreens and tactile sensors (Figure 22b). Their minimal power consumption enhances suitability for energy-sensitive systems. Nevertheless, fabrication challenges, susceptibility to environmental and crosstalk interference, and the need for shielding or calibration remain significant considerations. Piezoelectric sensors excel in dynamic stimuli detection and self-powering capabilities, making them indispensable for applications such as vibration monitoring and energy harvesting (Figure 22c). However, the fragility of piezoelectric materials and the complexity of their fabrication processes can elevate costs. Other mechanisms, including optical and magnetic sensors, present unique advantages. Optical sensors offer exceptional precision and stability for applications like biomedical devices and environmental monitoring (Figure 22d), albeit at the expense of high costs and complexity. Magnetic sensors, prized for their versatility and noncontact operation, face challenges in signal processing and mitigating interference (Figure 22e). \n\nAfter mechanism selection, optimizing device performance metrics is crucial for ensuring reliable and precise functionality. Sensitivity can be enhanced through advanced sensor architectures, incorporating hierarchical or nanostructured materials to amplify responses to external stimuli (Figure 22f). Stability, essential for long-term performance, can be improved with robust encapsulation and durable interface connections to mitigate environmental degradation (Figure $22\\mathbf{g})$ . Selectivity, the ability to target specific stimuli while avoiding interference, can be achieved using tailored response designs and advanced algorithms, including machine learning, to enhance precision (Figure 22h). Flexibility is critical for applications like wearable electronics and soft robotics, often achieved through the use of soft materials and innovative structural designs (Figure 22i). Dynamic applications, such as real-time monitoring, benefit from rapid response times, optimized through nanomaterial reinforcement and signalefficient designs (Figure 22j). Extending detection range involves advanced materials and hierarchical structures, enabling broader applicability (Figure 22k). Linearity, ensuring proportional input−output relationships, requires hybrid structures and computational approaches to minimize distortion (Figure 22l). Addressing hysteresis, which reflects discrepancies in cyclic loading and unloading, involves reducing interfacial friction and controlling elastic deformation (Figure $22\\mathrm{m}\\mathrm{,}$ ). Achieving these optimizations demands a multidisciplinary approach encompassing materials science, mechanical engineering, and machine learning. \n\nAfter decades of development, research on soft sensors focuses has increasingly shifted toward system integration, aiming to create devices that can be seamlessly deployed in real-world applications with reliability and convenience. To achieve this, comprehensive efforts are required to address critical aspects to make soft sensors into deployable systems, targeting multimodal sensing, high-density and large-area detection, efficient data acquisition, robust signal transmission, and sustainable power solutions. \n\nMultimodality allows sensors to detect diverse stimuli and perform sensor fusion for enriched data output. However, challenges such as mode interference, compatibility, and decoupling must be addressed to ensure robust performance (Figure 22n). High-density and large-area sensing is vital for robotics and interactive interfaces, requiring considerations of miniaturization and scalability (Figure 22o). In data acquisition and processing, balancing sampling rates with real-time analysis can be achieved through data compression and edge computing (Figure 22p). Wireless signal transmission is increasingly reliant on bandwidth optimization and interference reduction (Figure 22q). Flexible power solutions, such as advanced batteries and energy harvesting, enable long-term operation with reduced reliance on external sources (Figure 22r). These integrated efforts collectively drive the translation of soft sensors from fundamental research prototypes to practical technologies, paving the way for their deployment in real-world applications. \n\nIn short summary, as we already discussed a variety of working mechanism for soft sensors above, their advantages and disadvantages should be kept in mind for better and more efficient device design (Figure $22\\mathsf{a}\\mathrm{-}\\mathsf{e}$ ). For example, when the device is designed for detection of high-frequency stimuli, like the fast-adapting receptors in the skin, sensors based on a piezoelectric mechanism are preferred due to its inherent merits. After mechanism selection, the metrics of the device can be optimized, such as sensitivity, stability, selectivity, flexibility, response time, detection range, linearity, and hysteresis (Figure $22\\mathrm{f-m}$ ). Overall, the optimization of device metrics relies several key approaches: materials, structure engineering, fabrication, computational design, machine learning, etc.432−437 After decades of development, research on soft sensors focuses more and more on system integration, aiming to fabricate devices that can be immediately put to real world applications reliably and conveniently. For such integration, continuous efforts should be put on multimodality, high-density and large-area, data acquisition and processing, signal transmission, and power source, and more details can be found in Figure 22n−r.418,428−431 \n\n \nFigure 22. A summary for the future development of soft sensors in terms of mechanism selection, metrics optimization, and system integration. Advantages and disadvantages of the soft sensors based on five most common working mechanisms, including (a) resistive, (b) capacitive, (c) piezoelectric, (d) optical, and (e) magnetic. Definition and key strategies for optimization of the metrics of soft sensors, including (f) sensitivity, $(\\mathbf{g})$ stability, (h) selectivity, (i) flexibility, (j) response time, (k) detection range, (l) linearity, and $\\mathrm{(m)}$ hysteresis. Key points in the system integration level for soft sensors in terms of $\\mathbf{\\eta}(\\mathbf{n})$ multimodality, (o) high-density and large-area, (p) data acquisition and processing, (q) signal transmission, (r) power source. Reproduced with permission from ref 428. Copyright 2024 American Association for the Advancement of Science. Reproduced with permission from ref 429. Copyright 2024 Springer Nature. Reproduced with permission from ref 430. Copyright 2022 American Association for the Advancement of Science. Reproduced with permission from ref 418. Copyright 2023 Springer Nature. Reproduced with permission from ref 431. Copyright 2023 Springer Nature. \n\n \nFigure 23. Actuation mechanisms for different actuation modalities in soft robotics. (a) Fluidic actuation. (b) Magnetic actuation. (c) Chemical actuation. $(\\mathrm{d-g})$ Electroactive actuation: Dielectric elastomer actuators, hydraulically amplified actuators, and electrochemical actuators and piezoelectric actuators. $\\mathrm{(h,i)}$ Optical and thermal actuation: shape-memory actuators and liquid-crystal polymer actuators. $(\\mathrm{j-l})$ Other actuation modalities: Acoustically driven, biohybrid, and phase change actuators.",
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"chunk": "# 3. ACTUATION MODALITIES AND MATERIALS \n\nActuators are indispensable components of sensorimotor systems in the burgeoning field of soft robotics. Functioning analogously to motors, these actuators distinguish themselves from their traditional counterparts through their intrinsic flexibility and complianc e.108,117,438−442 Traditional machinery employs actuators and motors that are typically rigid, offering precision but sacrificing adaptability. In contrast, the innovative realm of soft robotics has pioneered the use of a vast array of materials engineered to perform various actuation modalities (Figure 23).443,444 \n\nFluidic actuation encompasses both pneumatic and hydraulic systems, which operate by building up positive pressure through gases and liquids, respectively.445,446 Magnetic actuation leverages the response of ferromagnetic or ferrimagnetic materials embedded within the actuators to applied magnetic fields.447,448 A significant class of actuation, electroactive actuations, includes dielectric elastomer actuators (DEAs), hydraulically amplified dielectric elastomer actuators (HADEAs), piezoelectric actuators, and electrochemical actuators. DEAs utilize extremely high electric fields (kilovolts) to deform elastomeric materials sandwiched between compliant electrodes, while in HADEAs, the elastomers are replaced with liquid dielectrics.449,450 Electrochemical actuators, operated at lower voltages, feature ionic polymer−metal composites (IPMCs) that utilize ion migration under electric fields.451,452 Additionally, the piezoelectric effect is employed in creating actuators for microrobotic applications.453,454 Chemical actuations include responsive systems that utilize bimorph or gradient structures responding to the absorption/ desorption of water/solvents or changes in $\\mathsf{p H}$ , typically resulting from the swelling/deswelling of responsive materials.455,456 Enzyme-mediated actuations represent an intriguing category where chemical reactions drive actuation.457,458 Furthermore, optical and thermal actuation modalities, such as light-responsive liquid-crystal elastomer (LCE) and liquidcrystal polymer (LCP) actuators, as well as typical thermalresponsive actuators like shape-memory alloys (SMAs) and polymers, play significant roles.459−462 The field also explores novel actuations including acoustically driven, biohybrid, and phase change actuators.463−465 \n\n \nFigure 24. Summary of static and dynamic performance characteristics of actuators: (a) Illustration of the relationship between maximum stroke and maximum force output. (b) Graph of force density versus actuation speed. (c) Graph showing the correlation between power density and work density. (d) Graph showing the relationship between power density and actuation efficiency. Reproduced with permission from ref 467. Copyright 2021 The Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND). (e) Diagram showing the relationship between output force, strain, response speed, and driven voltage for four main types of electroactive actuators: electrochemical, piezoelectric, HASEL, and DEA actuators. Reproduced with permission from ref 550. Copyright 2020 Wiley. Reproduced with permission from ref 454. Copyright 2019 Springer Nature. Reproduced with permission from ref 479. Copyright 2018 American Association for the Advancement of Science. Reproduced with permission from ref 449. Copyright 2019 American Association for the Advancement of Science. \n\n \nFigure 25. Fluidic actuations for soft robotics. (a) Illustration of a robotic spider of a soft robotic spider constructed from laminated multilayers utilizing lithography and laser micromachining techniques. (b) Time-lapse images of an untethered soft robotic fish powered by soft electronic pump performing swimming motion underwater. (c) Images of an untethered soft gripper that can swiftly seize a falling ping-pong ball. (d) Image of a third-generation of pleated pneumatic artificial muscle (PPAM) device. (e) Image of an electronics-free autonomous gripper fabricated using desktop fused filament fabrication (FFF) three-dimensional printing. (f) Image of an electronics-free pneumatic soft-legged quadruped robot. (g) Walking pneumatic-driven soft robot with embedded hysteretic valves. (h) Commercialized collaborative robots from Festo Pte. Ltd. (a) Reproduced with permission from ref 468. Copyright 2018 Wiley. (b) Reproduced with permission from ref 469. Copyright 2021, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (c) Reproduced with permission from ref 470. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (d) Reproduced with permission from ref 471. Copyright 2012, Taylor & Fransis. (e) Reproduced with permission from ref 164. Copyright 2023 American Association for the Advancement of Science. (f) Reproduced with permission from ref 473. Copyright 2021 American Association for the Advancement of Science. (g) Reproduced with permission from ref 474. Copyright 2022 Elsevier. (h) Reproduced with permission from ref 472. Copyright 2020 Elsevier. \n\nEach modality leverages unique physical principles and material properties to fulfill specific functional requirements in soft robotic systems.444,466 Pneumatic and hydraulic actuators, widely used for their high actuation force and speed, large actuation stroke, and work density (Figure $24\\mathsf{a}-\\mathsf{d},$ ), are often coupled with a pumping control system, which limits their untethered applications. Electrically based actuators integrate well with electronic sensing and control systems, showing promise for AI-driven robotic systems of the future. DEA actuators display the highest actuation strain, speed, and power density, though they are limited by high operating voltages (>kilovolts). Efforts are underway to develop low-voltage DEAs. In contrast, IPMC actuators can be operated at voltages of as low as $_{1-3\\mathrm{V}}$ . But they display the lowest actuation strain and power density, making them only suitable for applications requiring minimal actuation. Magnetic, optical, and thermal actuators are all well-suited for untethered robot applications; magnetic actuation, for instance, provides high force and speed but requires sophisticated machinery to manipulate the magnetic field. LCE actuators outperform IPMCs in actuation strain and power density but exhibit even slower response speeds. As for thermal actuators, SMAs possess the highest work and power densities. However, typical SMA actuators have limited applications in soft robotics due to the rigid components. Novel shape-memory polymer actuators made from elastomers show potential for broader use in soft robotics. Nevertheless, one performance of actuators may be affected by another. For example, the actuation performance of electroactive actuators is indeed highly dependent on the driving voltages, and there is often a trade-off between the performance indicators, such as output force, strain, and respond speed, as shown in Figure 24e. Thus, such trade-off effects should be considered when designing actuators for a specific application. \n\nThis section provides an in-depth exploration of various actuation modalities commonly employed in soft robotics. It delves into the scientific principles underpinning each type of actuation, detailing the mechanisms that drive their functionality and the materials essential for their construction. Furthermore, a comprehensive comparison of these modalities is presented, assessing their respective advantages and disadvantages with respect to actuation performance, efficiency, and control complexity, offering practical guidance on selecting and implementing these actuators in soft robotics applications.",
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"chunk": "# 3.1. Fluidic Actuators \n\nFluidic actuators operate primarily through the application of pressure, typically positive pressure, to achieve deformation. They are capable of generating significantly high actuation forces and speeds, making them a popular choice across various applications. As a result, numerous commercial products, such as pneumatic grippers, have become widely utilized within the industry. Despite their widespread adoption and utility, one major limitation is their dependency on relatively large-sized external rigid pumps, which can hinder their integration into untethered soft robotics where compactness and flexibility are crucial. To resolve such an issue, innovative designed have been made to achieve soft pumping systems. In the forthcoming sections, we will provide a detailed exploration of both hydraulic and pneumatic actuators, including operational mechanisms, typical applications, and the specific challenges they face. \n\n3.1.1. Hydraulic. Hydraulic actuators function by injecting fluid into specifically engineered chambers, where pressure accumulation enables diverse movements, such as bending and twisting. These movements are integral for various tasks including object manipulation, emulation of biological mechanisms, and implementing locomotion. Ranzani et al. introduced the microfluidic origami reconfigurable pneumatic/ hydraulic (MORPH) actuation system.468 Through the utilization of lithography and laser micromachining, they produced soft laminated multilayers featuring embedded microfluidic channels arranged in complex, arbitrary structures. The multilayer soft lithography process enables the conversion of 2D laminates into 3D devices. Illustrated in Figure 25a, a MORPH system composed of 12 layers of laminates was assembled to replicate the movements of a peacock spider, boasting 9 independently controllable degrees of freedom (DoFs) and 5 structural DoFs. \n\nAs mentioned earlier, conventional fluidic actuators often rely on bulky rigid pumping systems, posing a challenge. To address this issue, drawing inspiration from spider hydraulic systems, Zou’s group devised a soft electronic pumping system utilizing an electrohydrodynamic (EHD) mechanism, offering superior actuation performance coupled with self-healing capability.469 The operational principle of these soft electronic pumps hinges on the application of a strong nonuniform electric field between positive and grounding electrodes. During this process, electrons near the positive electrodes overcome potential barriers and dissociate from neutral liquid molecules, transforming into free electrons. These electrons are then absorbed into the positive electrodes, leaving behind positively charged ions. Driven by Coulomb forces, these ions migrate toward the grounding electrodes, dragging neutral liquid molecules with them through a hole, generating a robust jet. Upon reaching the grounding electrodes, the ions recombine with electrons on the electrode surfaces, restoring neutral liquid molecules. This continuous flow persists through the migration of electrons and ions under the electric field, ceasing only when the field is removed. The self-healing mechanism involves a functional liquid composed of dibutyl sebacate and tung oil solution, where tung oil’s solidification properties, triggered by exposure to air, facilitate automatic repair of damages in soft robots. Figure 25b illustrates an untethered soft robotic fish, propelled by the soft electronic pump, demonstrating swimming motion underwater. By incorporating soft EHD pumps, actuators, healing electrofluids, and E-skins, they have developed soft fluidic robots distinguished by their rapid actuation and advanced selfprotection capabilities.470 Utilizing this technology, Figure 25c demonstrates an untethered soft gripper that can swiftly seize a falling ping-pong ball, dropped from a height at a speed of 65 $\\mathrm{cm}/\\mathrm{s},$ controlled manually through a smartphone app. \n\n3.1.2. Pneumatic. Since Joseph L. McKibben pioneered the first basic soft fluidic robot, known as the McKibben artificial muscle, in the 1950s, extensive research has been dedicated to refining the design, actuation, sensing, control, and applications of such robots.471 Pneumatic artificial muscles (PAM) find widespread use in walking robots and rehabilitation devices, with various forms commercialized by companies.472 These muscles, including the well-known McKibben muscle, typically consist of a rubber inner tube expanding when inflated, with tension transferred by a braided sleeving, albeit with drawbacks like dry friction and rubber tube deformation. To address these issues, the pleated pneumatic artificial muscle (PPAM) was developed, featuring a mathematical model ensuring accurate performance prediction.471 The PPAM’s innovative design allows operation at low pressures, ranging from 20 mbar to 4 bar gauge pressure, with contractions exceeding $40\\%$ . A third generation of PPAM, presented by Villegas et al., simplifies manufacturing and improves durability, leveraging fused deposition modeling (FDM) rapid prototyping for complex and lightweight end closures (Figure 25d).471 Unlike previous generations, continuous high-tensile fibers are now integrated into toothed end closures and folded membranes, streamlining production and reducing weight. \n\nPneumatic actuators are manufactured through molding and assembly processes, which often involve numerous manual steps and limit complexity. Additionally, integrating complex control components, such as electronic pumps and microcontrollers, is necessary to achieve even basic functions. Desktop fused filament fabrication (FFF) three-dimensional printing offers a more accessible alternative with reduced manual labor and the ability to produce intricate structures. However, FFF-printed soft robots commonly exhibit high effective stiffness and numerous leaks due to material and process constraints, restricting their utility. Zhai et al. introduced a design approach for 3D printing monolithic, airtight, high-performance soft pneumatic robots using a commercially available desktop FFF printer.164 Key design principles include printing structures with a single continuous toolpath, known as an Eulerian path, to ensure airtightness, and creating thin-walled structures with low stiffness, comparable to silicone-molded parts, when combined with the first principle. Following this design methodology, a \n\n \nFigure 26. Electroactive soft actuators. (a) Photograph of the soft dynamic DEA valves. (b) Photograph of a transparent loudspeaker. (c) Photograph of the untethered insect robot based on low-voltage DEA. (d) Photograph of the pipeline inspection robots driven by DEA. (e) A soft gripper based on two modified stacks of donut-shaped HASEL actuators for handle delicate objects. (f) Photograph of EBM arrays with of six pouches. $\\mathbf{\\eta}(\\mathbf{g})$ Photograph of the ${\\boldsymbol{\\mathfrak{s}}}\\times{\\boldsymbol{\\mathfrak{s}}}$ array of HAXELs actuators. (h) Photograph of a five-pouch HALVE actuator equipped with chrome/gold electrodes, lifting its $_{13-\\mathrm{g}}$ power supply. (i) Optical image of the 3D microscale mechanical frameworks with piezoelectric thin-film actuators. (j) Photograph of the untethered insect-sized flapping-wing MAV. (k) Photograph of the configuration of the insect-scale fast moving soft robot. (l) Photograph of the BFFSPR robot climbing a $12^{\\circ}$ slope. $\\mathrm{(m)}$ Schematic representation of the actuation mechanism in the Graphdiyne-Based Electrochemical Actuator. (n) Demonstration of the MXene-based electrochemical acuators in a kinetic art. (o) Photographs of the $\\mathbf{MoS}_{2}$ -based actuator lifting an object at $0.3\\mathrm{V}$ . (p) Photographs of the nickel nanowire-forest-based actuator deforming. (a) Reproduced with permission from ref 476. Copyright 2021 National Academy of Sciences. (b) Reproduced with permission from ref 477. Copyright 2013 American Association for the Advancement of Science. (c) Reproduced with permission from ref 449. Copyright 2019 American Association for the Advancement of Science. (d) Reproduced with permission from ref 478. Copyright 2022 American Association for the Advancement of Science. (e) Reproduced with permission from ref 479. Copyright 2018 American Association for the Advancement of Science. (f) Reproduced with permission from ref 481. Copyright 2021 American Association for the Advancement of Science. $(\\mathbf{g})$ Reproduced with permission from ref 480. Copyright 2020 Wiley. (h) Reproduced with permission from ref 482. Copyright 2024, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). (i) Reproduced with permission from ref 483. Copyright 2018, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). (j) Reproduced with permission from ref \n\n454. Copyright 2019 Springer Nature. (k) Reproduced with permission from ref 484. Copyright 2019 American Association for the Advancement of Science. (l) Reproduced with permission from ref 485. Copyright 2023 Wiley. (m) Reproduced with permission from ref 486. Copyright 2018, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (n) Reproduced with permission from ref 487. Copyright 2019 American Association for the Advancement of Science. (o) Reproduced with permission from ref 488. Copyright 2017 Springer Nature. (p) Reproduced with permission from ref 489. Copyright 2016 Wiley. \n\ncustomized electronics-free pneumatically driven autonomous gripper was fabricated (Figure 25e). Traditionally, the control of such pneumatic robots has relied on electromechanical components like valves and pumps, which tend to be bulky and costly. Tolley’s group introduced a novel method for governing the movements of soft-legged robots using basic pneumatic circuits, completely devoid of electronic elements.473 This method orchestrates locomotion patterns through ring oscillators composed of soft valves, generating oscillatory signals akin to biological central pattern generator neural circuits. Pneumatic logic components respond to sensor inputs, directing the actions of these oscillators. They illustrate this method by designing pneumatic control circuits capable of generating walking patterns for a soft-legged quadruped with three degrees of freedom per leg, and seamlessly switching between gaits to regulate the direction of movement (Figure 25f). There are also other interesting pneumatic-driven soft robots, such as the walking soft robot with embedded hysteretic valves and elephant trunk-inspired soft robotic arm (Figure 25g,h).472,474",
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"chunk": "# 3.2. Electroactive Actuators \n\nElectrically responsive actuators are particularly well-suited for integration with electronic sensing and control systems, making them foundational components in the development of sophisticated, AI-driven robotic systems.475 Their inherent compatibility with electronic technologies allows for seamless communication and synchronization between sensors, actuators, and controllers. This integration facilitates precise, realtime adjustments to actuator behavior based on continuous sensory feedback, which is crucial for adaptive and intelligent robotic functioning. \n\n3.2.1. Dielectric Elastomer Actuators. Dielectric elastomer actuators (DEAs) are highly valued in soft robotics due to their exceptional energy density, efficiency, flexibility, and lightweight design. These attributes make them ideal for overcoming limitations in traditional fluid-driven soft robots, which often suffer from rigid and limiting control systems. To enhance adaptability and mobility, various forms of soft valves for fluidic actuators have been developed, notably those driven electrically. A significant breakthrough was achieved by the Wood group, who developed an electrically powered soft valve for hydraulic actuators equipped with mesoscale channels (Figure 26a).476 This innovation utilizes a new class of ultrahigh-power density dynamic DEAs, which are capable of operating at frequencies of ${500}\\mathrm{Hz}$ or higher. These advanced actuators generate a blocked force that is $300\\%$ greater than that of previously used dynamic DEAs. Moreover, they achieve a loaded power density of $290\\mathrm{\\W\\bulletkg^{-1}}$ under operating conditions, marking a substantial enhancement in both performance and efficiency for applications in soft robotics. Suo’s group achieved a notable breakthrough with the development of a transparent loudspeaker that can produce sound over the entire audible spectrum, ranging from $20\\ \\mathrm{Hz}$ to \n\n$20~\\mathrm{kHz}$ (Figure $26\\mathsf{b}$ ).477 They crafted this innovative device using $1\\ \\mathrm{mm}$ thick VHB 4910 tape (3M) as the dielectric layer and a $100\\mathrm{-}\\mu\\mathrm{m}$ thick polyacrylamide hydrogel containing NaCl as the electrolyte. The loudspeaker’s effectiveness was demonstrated through a spectrogram of the recorded sound, which successfully replicated the main signal of the original test sound across the entire range of audible frequencies. Shea’s group has enhanced the capabilities of dielectric elastomer actuators (DEAs), which are typically known for kilohertz operation and high power density but require several kilovolts to achieve full strain.449 The mass of these kilovolt power supplies has traditionally limited the speed and performance of DEA-driven robots. In their recent work, they introduced DEAnsect: an autonomous, insect-sized ( $40\\ \\mathrm{mm}$ long), fast 1 $\\cdot30\\mathrm{\\mm/s}$ tethered, $12\\mathrm{mm}/s$ untethered), and ultralight $\\left(1\\ \\mathrm{g}\\right)$ legged soft robot (Figure 26c). Equipped with integrated sensors, power units, battery, and control electronics, DEAnsect is powered by three low-voltage stacked DEAs (LVSDEAs), each operating a leg at $450\\mathrm{~V~}$ and over $600~\\mathrm{Hz}$ . Despite its minimal weight, with a $190\\mathrm{-mg}$ body and $780~\\mathrm{mg}$ of onboard electronics. In addition, Tao et al. developed a pipeline inspection robot designed to navigate pipes with subcentimeter diameters and various curvatures The robot utilizes high-power DEAs for its elongation units and smart composite microstructure (SCM)-based, high-efficiency transmissions for its anchoring units (Figure 26d).478 Through meticulous modeling and analysis of the robot’s dynamic characteristics, as well as precise tuning of the activation voltages’ frequencies and phases, this pipeline robot can achieve rapid motion in various directions, surpassing 1 body length per second in subcentimeter-sized pipes.",
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"chunk": "# \n\n3.2.2. Hydraulically Amplified Electrostatic Actuators. Dielectric elastomer actuators (DEAs) are known for their high actuation strain and efficiency, yet they are prone to failures such as dielectric breakdown and electrical aging due to the high electric fields required for operation. In response to these limitations, Keplinger’s group has introduced a pioneering class of high-performance, muscle-mimetic soft transducers named hydraulically amplified self-healing electrostatic (HASEL) actuators.479 These actuators employ an electrohydraulic mechanism to activate all-soft matter hydraulic architectures, blending the advantages of soft fluidic actuators with the muscle-like performance and self-sensing capabilities of DEAs. Unlike conventional soft fluidic actuators that experience inefficiencies due to fluid transport through channel systems, HASEL actuators create hydraulic pressure locally via electrostatic forces acting on liquid dielectrics integrated within the soft structure. This novel application of liquid dielectrics not only boosts efficiency but also facilitates self-healing, enabling the actuators to instantly regain functionality after experiencing multiple dielectric breakdowns. Figure 26e demonstrates a soft gripper crafted from two modified stacks of donut-shaped HASEL actuators. This gripper is specifically designed to handle delicate objects, such as a raspberry. Fontana’s group developed an electrostatic actuator called the circular electrostatic bellow muscle (EBM), made from thin films, liquid dielectrics, and rigid polymeric stiffeners. This unit is designed for out-of-plane contraction, is easy to manufacture, and can be configured into arrays or stacks. EBMs function as contractile artificial muscles, pumps for fluid-driven soft robots, or energy harvesters. With diameters ranging from 20 to $40\\ \\mathrm{mm}$ , these EBMs can exert forces up to $\\bar{6}\\mathrm{~N~}$ , lift over a hundred times their weight, and achieve contractions over $40\\%$ with strain rates surpassing $120\\%$ per second and a bandwidth above 10 Hz. By arranging six in-series EBM pouches in a $1\\times6$ layout (Figure 26f), strokes of up to $43\\%$ of the muscle’s initial length were achieved. Shea’s group has addressed a long-standing challenge in soft actuators\u0001creating thin devices that combine high force and large displacement\u0001through the development of a new type of actuator known as the hydraulically amplified taxel (HAXEL). This innovative design features a fluid-filled cavity enclosed by a nonstretchable polymer shell with an elastic central top, allowing significant expansion when activated. The use of materials with high breakdown voltage enhances energy efficiency, while the thin, flexible design makes HAXEL arrays ideal for immersive virtual reality applications, such as tactile gloves, bracelets, and customizable body patches (Figure 26g).480 \n\nTo lower the operating voltages, Katzschmann’s group developed the hydraulically amplified low-voltage electrostatic (HALVE) actuator, achieving an average power density of 50.5 ${\\mathrm{W/kg}}$ and a peak strain rate of $971\\%$ per second at $1100~\\mathrm{V}_{;}$ , comparable to mammalian skeletal muscle.482 This actuator is safe to touch, waterproof, and self-clearing, making it suitable for robotics and wearables. It uses a three-part design: a strong polymer outer shell, an electrode, and a high-energy density dielectric layer of P(VDF-TrFE-CTFE), enhancing electrostatic performance while lowering voltage requirements. The central cavity filled with dielectric oil amplifies the hydraulic effect. Using the Peano-HASEL actuator geometry, the device operates below 1300 V. When voltage is applied, Coulomb forces attract the electrodes, displacing the dielectric oil and deforming the actuator into a cylindrical shape that performs mechanical work. This modular structure enhances the actuator’s efficiency and application versatility (Figure 26h). \n\n3.2.3. Piezoelectric Actuators. Recent advances have heightened interest in the development of subgram vehicles, prized for their high maneuverability\u0001thanks to favorable torque and inertia scaling\u0001and their ability to perform tasks such as environmental monitoring and navigation in confined spaces. As micro aerial vehicles (MAVs) become smaller, traditional actuation mechanisms and bearings encounter significant challenges. These include reduced efficiency of electromagnetic motors and heightened frictional losses due to unfavorable scaling laws. Consequently, piezoelectric actuators, whose power density scales inversely with their length, are favored in microscale applications. Their ability to perform oscillatory operations is particularly well-suited to the flapping motions required by MAVs. Ning et al. have furthered the capabilities of mechanically active 3D MEMS by developing complex 3D mesoscale architectures that incorporate integrated piezoelectric actuators under independent electrical control (Figure 26i).483 This allows for the dynamic excitation of selected vibrational modes. Innovative transfer printing techniques facilitate the integration of ultrathin piezoelectric films and ductile metals onto polymer layers, which are lithographically patterned into 2D geometries. Controlled mechanical buckling processes then convert these 2D multifunctional material structures into precisely defined 3D architectures. These structures are versatile enough to be deployed onto both flat and curved surfaces, accommodating a variety of substrate types. Wood’s group successfully achieved sustained untethered flight with an insect-sized flapping-wing MAV (Figure 26j).454 They tackled the integration challenges of onboard electronics within a constrained payload capacity and achieved a lift-to-weight ratio surpassing that of typical biological counterparts. The vehicle features four wings powered by two alumina-reinforced piezoelectric actuators, enhancing its aerodynamic efficiency. This integrated system weighs just $259~\\mathrm{\\mg}$ and operates on a mere 110−120 milliwatts of power. To optimize efficiency, the authors finetuned the drive signals, reducing both power consumption and the mass and complexity of the drive electronics. The design improvements included a new actuator made from micromachined alumina and thorough benchtop tests to confirm the wing kinematics and system frequency response. Ultimately, the vehicle demonstrated successful untethered flight powered by solar cells, marking significant advancements in the performance and efficiency of flapping-wing MAVs driven by piezoelectric actuators. Lin’s group has developed a fast and ultrarobust insect-scale soft robot, inspired by the dynamics of animal locomotion.484 This robot can travel at speeds of up to 20 body lengths per second, endure the weight of an adult stepping on it, carry loads six times its own weight, and efficiently climb slopes (Figure 26k). The enhancements in its design include optimized geometric parameters and the addition of a back leg, which significantly increased its speed. Yin’s group has developed a soft robot named the Bio-Mimic, Fast-Moving, and Flippable Soft Piezoelectric Robot (BFFSPR), inspired by the rapid and agile gait of cheetahs.485 This robot employs a double spiral structure coupled with a piezoelectric actuator to facilitate high-speed movement and exceptional agility, making it highly adaptable to complex environments (Figure 26l). \n\n3.2.4. Electrochemical Actuators. Electrochemical or ionic polymer−metal composite (IPMC) actuators are a type of soft actuator that leverage ionic movement within a polymer matrix under an electric field to create motion. These actuators are known for their low voltage operation, flexibility, and the ability to produce large bending or twisting motions, which make them highly suitable for soft robotics. In soft robotics, IPMC actuators are particularly valued for their biomimetic properties, allowing them to mimic the gentle yet complex movements of biological organisms. This capability makes them ideal for applications where delicate interaction with the environment is necessary, such as in medical devices for minimally invasive surgeries or wearable technology that interacts directly with the human body. Their inherent softness and compliance also enable the design of robots that can safely operate in unstructured and dynamic environments, further expanding the scope of robotic applications to areas that require a high degree of adaptability and safety. \n\nNovel materials have been used for the fabrication of electrochemical actuators. Chen’ group developed a highperformance electrochemical actuator based on graphdiyne.486 The actuator exhibited an unprecedented electro-mechanical transduction efficiency of up to $6.03\\%$ and maintained excellent performance over 100,000 cycles. The researchers identified the alkene−alkyne complex transition within the \n\n \nFigure 27. Soft robot actuated by magnetism. (a) 3D-printed ferromagnetic actuator featuring a hexapedal structure, which wraps around and transports an oblong pharmaceutical pill using rolling-based locomotion. (b) Images of the multimodal small-scaled robot climbed a water meniscus, landed on a solid platform, jumped over an obstacle, and walked away. (c) Photograph of the multilayer soft robot for on-demand multitargeted adhesion. (d) Image of the scaled robot inspired by the overlapping design of the pangolin is shown on the right. (e) Images showing the folding of the MaSoChain and disassembly. (f) Images showing the swimming motion of the jellyfish-like robot visualized by fluorescein dye. (g) Photograph of the miniature magnetic gripper lifting a PDMS cube. (h) Photographs illustrating the strain engineering process used to create helical structures for incorporating magnetic composites. (i) Optical images of ferrofluid droplets navigating through a circularly curved channel, a sharp turn, and a gap. (j) Illustration and video snapshots depict the deformation, cargo delivery, and splitting of a liquid metal magnetic soft robot (LMMSR). (k) Microscopic images showing hematite colloidal particles forming liquid, chain, vortex, and ribbon swarming patterns under magnetic fields. (l) Image of the microdisks forming a pattern with 5-fold symmetry. (m) Images showing the rheotaxis of the acousto-manetic microswarm rolling along the capillary wall under a combined acoustic and magnetic field. (a) Reproduced with permission from ref 111. Copyright 2018 Springer Nature. (b) Reproduced with permission from ref 490. Copyright 2018 Springer Nature. (c) Reproduced with permission from ref 109. Copyright 2024, The Authors, published by Springer Nature. (d) Reproduced under the terms of the Creative Commons Attribution 4.0 International License. Reproduced with permission from ref 491. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (e) Reproduced with permission from ref 492. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (f) Reproduced with permission from ref 493. Copyright 2019, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (g) Reproduced with permission from ref 494. Copyright 2018 Wiley. (h) Reproduced with permission from ref 495. Copyright 2023 Wiley. (i) Reproduced with permission from ref 496. Copyright 2022, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (j) Reproduced with permission \n\nfrom ref 497. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (k) Reproduced with permission from ref 498. Copyright 2019 American Association for the Advancement of Science. (l) Reproduced with permission from ref 499. Copyright 2022, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). $\\mathbf{\\tau}(\\mathbf{m})$ Reproduced with permission from ref 500. Copyright 2021 Springer Nature. \n\ngraphdiyne structure as the key mechanism behind the enhanced performance, which was verified using in situ sum frequency generation spectroscopy (Figure $26\\mathrm{m}\\dot{}$ ). In addition to graphdiyne, MXene has also been utilized in significant advancements within soft robotics.487 Oh’s group developed MXene artificial muscles that showcase ultrafast response times of under one second, exceptional bending strains up to $1.37\\%$ , and durable cyclic stability for up to 18,000 cycles (Figure $26\\mathbf{n}\\mathrm{\\dot{\\Omega}}$ ). These muscles maintain high structural reliability across various frequencies and voltages without delamination, thanks to the use of a $\\mathrm{Ti}_{3}\\mathrm{C}_{2}\\mathrm{T}_{\\it x}$ electrode ionically cross-linked with poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate). Demonstrating their versatility, practical applications of these MXene-based actuators include an origami-inspired wearable brooch and kinetic art pieces, highlighting their potential for innovative robotic devices. Acerce utilized two-dimensional metallic molybdenum disulfide $(\\mathbf{MoS}_{2})$ to achieve a low operating voltage of just $0.3\\mathrm{~V~}$ while developing a robust electrochemical actuator.488 This actuator, constructed from chemically exfoliated $\\mathbf{MoS}_{2}$ nanosheets on thin plastic substrates, demonstrated the ability to generate significant mechanical forces. It could lift masses more than 150 times heavier than the electrode itself over several millimeters for hundreds of cycles (Figure 26o). The films produced mechanical stresses around $17\\ \\mathrm{MPa}$ \u0001on par with ceramic piezoelectric actuators\u0001and sustained strains up to $0.6\\%$ , functioning effectively at frequencies up to $1\\ \\mathrm{Hz}$ . This high performance is credited to the excellent electrical conductivity of the metallic 1T phase of $\\mathrm{MoS}_{2},$ the significant elastic modulus of the restacked $\\mathbf{MoS}_{2}$ layers, and rapid proton diffusion through the nanosheets. These advancements herald the potential for new electrochemical actuators designed for high-strain and high-frequency uses. Cheng et al. developed a high-performance electrochemical actuator using a unique three-dimensional anodic aluminum oxide (AAO) template filled with nickel nanowires.489 These nanowires are organized into a dual-level “nanowire-forest” structure that enhances ion transport and actuation strain due to their high surface-area-tovolume ratio and mechanical stability. The system demonstrates a rapid and reversible actuation mechanism, capable of significant mechanical displacement observable by the naked eye, with potential applications in robotics and microelectromechanical systems (Figure 26p).",
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"chunk": "# 3.3. Magnetic Actuators \n\nMagnetic actuators leverage the force generated by magnetic fields to drive movement and are increasingly used in the field of soft robotics due to their unique advantages. These actuators provide wireless actuation, allowing for greater flexibility and adaptability in designing robots that can operate in intricate or harsh environments without the need for physical connections. In soft robotics, magnetic actuators are especially valued for their ability to produce precise, controlled movements through noncontact means, making them ideal for applications requiring gentle and complex manipulations, such as in biomedical devices or when handling delicate materials. Furthermore, their capacity for rapid response and reversibility enhances the functionality of soft robotic systems, enabling them to perform tasks with higher efficiency and adaptability, ranging from targeted drug delivery within the human body to intricate object manipulation in unstructured settings. \n\n3.3.1. Solid-state Magnetic Robots. While solid-state magnetic actuators have been extensively explored, Zhao’s group has made significant advancements by developing a method for 3D printing soft materials with programmed ferromagnetic domains.111 Utilizing a composite ink composed of magnetizable microparticles and fumed silica nanoparticles embedded in a silicone rubber matrix, they applied a magnetic field during the printing process to induce patterned magnetic polarity in the printed filaments. This innovative approach enabled the programming of complex 3D shapes capable of undergoing rapid transformations under magnetic actuation. The resulting structures demonstrated high actuation speed and power density, surpassing those of existing 3D-printed active materials. The team expanded this technique to create high-aspect-ratio multilayered and auxetic structures with negative Poisson’s ratios. They showcased various functionalities such as reconfigurable soft electronics, mechanical metamaterials, and soft robots that can crawl, roll, catch objects, and transport pharmaceutical doses (Figure 27a). Sitti’s group has developed a versatile soft robot capable of multiple locomotion modes including walking, rolling, and undulating swimming (Figure 27b).490 This robot has demonstrated its ability to navigate through a synthetic stomach phantom, move within ex vivo chicken tissue under ultrasound guidance, and grip, transport, and selectively release cargo. Yu’s group developed a magnetic multilayer soft robot for targeted adhesion in the stomach. They fabricated the robot using a magnetic film, an adhesive film, and different layers assembled through laser cutting and plasma treatment (Figure 27c).109 Sitti’s group has advanced the development of untethered miniature robots capable of on-demand heating for biomedical applications, inspired by the unique structure of pangolins (Figure 27d). These bilayered soft robots demonstrated multiple functionalities, including selective cargo release, in situ demagnetization, hyperthermia treatment, and bleeding control, optimized for efficient Joule heating. The robots have shown promise for medical applications such as stopping bleeding and administering hyperthermia therapy. Furthermore, the team developed a wireless magnetic soft millirobot equipped for thermal adhesive bonding, cargo release, and targeted drug delivery. This robot was thoroughly evaluated for its heating performance, mechanical properties, and deformation capabilities.491 \n\nNelson’s group has developed magnetic soft-robotic chains (MaSoChains), a novel magnetic soft-robotic chains capable of self-folding into programmable shapes.492 These chains are made from alternating soft and rigid segments, assembled with NdFeB magnets, allowing them to transform into various functional structures such as grippers and tethered capsule endoscopes (Figure 27e). Additionally, the integration of flexible printed circuit boards (PCBs) within the MaSoChains has expanded their functionality by incorporating electronic components. This study introduces an innovative approach to designing self-folding soft-robotic chains with reconfigurable shapes and functionalities, showing great potential for applications in minimally invasive surgeries. Sitti’s group also developed a soft millirobot inspired by the ephyra, a juvenile stage of jellyfish, capable of manipulating fluidic flow to perform various functions and tasks. This robot can be actuated using magnetic fields, biological muscle cells, or shape-memory alloys to achieve diverse swimming modes (Figure 27f). The team also explored the robot’s ability to retain objects, uncovering two mechanisms that could lead to object escape.493 Visel’s group has designed and fabricated miniature soft electromagnetic actuators (EMAs) using silicone polymer, liquid metal alloy (EGaIn), and magnetic powder.494 These actuators incorporate 3D helical coil conductors as electromagnetic inductors and find use in applications like soft vibrotactile actuators (SVAs) and miniature soft electromagnetic grippers (SEMGs) (Figure $27\\mathbf{g})$ . This work addressed challenges in creating highperformance EMAs and introduces new methods for designing and fabricating soft composite structures. Anikeeva’s group has developed 3D magnetic soft robots using fiber-based actuators and magnetic elastomer composites, controlled by unidirectional magnetic fields.495 They crafted helical structures from thermally drawn elastomeric fibers embedded with a magnetic composite (Figure 27h). By adjusting strain and magnetization treatments, they created worm-like crawlers and bipedal walkers capable of moving in magnetic fields orthogonal to their plane of motion. The robots demonstrated functionalities such as cargo carrying, and multiple robots could be simultaneously controlled\u0001even in opposing directions\u0001 with a single stationary electromagnet. \n\n3.3.2. Liquid-State Magnetic Robots. Magnetic liquidbased actuators and robots represent another significant category in the field. Zhang’s group conducted experiments with hydrocarbon oil-based ferrofluids to understand the dynamics and behavior of ferrofluid droplets across various terrains. Using both positive and negative casting techniques, along with 3D printing technology, they crafted liquid cilia arrays to interact with the droplets. The team developed magnetic actuation systems to precisely control these droplets and applied mathematical equations to model their behavior (Figure 27i).496 They also delved into the wetting dynamics of ferrofluid droplets on different substrates, demonstrating multiple motion modes. Furthermore, the research explored the applications of ferrofluid droplets as liquid capsules, liquid cilia, and liquid skin in the creation of miniature soft machines, highlighting their potential in biomedical applications. Ma’s group developed a magnetic liquid metal composite by integrating iron oxide magnetic nanoparticles into eutectic gallium indium liquid metal through a reactive wetting mechanism.497 To enhance wettability between the magnetic nanoparticles and the liquid metal, a silver intermediate layer was introduced. The resulting composite displayed excellent suspension stability and magnetism. Utilizing this composite, the researchers crafted a miniature soft robot that demonstrated controlled deformation and locomotion under an external magnetic field (Figure 27j). The composite’s biocompatibility was confirmed as nontoxic to normal cells, and it exhibited remarkable stability in the stomach’s acidic environment. Additionally, the remote magnetic manipulation of the soft robot was successfully demonstrated using an imaging system. This study not only advances the development of magnetic liquid metal composites for biomedical applications in miniature soft robots but also suggests a new composite preparation strategy that could enhance the wetting conditions between liquid metal and various inorganic nonmetallic materials. \n\n3.3.3. Magnetic Robot Swarm. Magnetic robot swarms represent a cutting-edge innovation in the field of microrobotics, harnessing the principles of magnetism to control and manipulate small-scale robotic systems collectively. These swarms are composed of tiny individual robots, each embedded with magnetic materials, allowing them to respond dynamically to magnetic fields. This response enables the coordinated control of large groups of robots, facilitating complex, synchronized movements that mimic natural swarms found in biological systems. Xie et al. have developed a system of magnetic microrobots capable of forming various collective formations\u0001liquid, chain, vortex, and ribbon\u0001in response to programmed alternating magnetic fields (Figure 27k). They explored how these microrobots can navigate narrow channels, handle substantial loads, and perform synchronized manipulations. The team characterized the physical mechanisms driving the dynamics of these microrobotic swarms and created a computational model to simulate the observed phenomena accurately. The study demonstrated the swarms’ ability to precisely follow planned paths, navigate through constricted spaces, and conduct collective tasks, showcasing the versatility and control of reconfigurable microrobot swarms for a range of practical applications.4 Sitti’s group conducted a comprehensive study on the interrelationships among information, structure, and interactions within a system of microdisks (Figure 27l). Utilizing a combination of experimental observations, numerical simulations, and theoretical calculations, they gained insights into these crucial connections. They revealed direct links between information, structures, and interactions, highlighting the influence of neighbor distances in pattern formation.499 Nelson’s group has made significant advances in the manipulation and control of microparticles using acoustic and magnetic fields. They developed an acoustofluidic device capable of manipulating particles with ultrasound and engineered an experimental setup to control microparticles using both acoustic and magnetic fields simultaneously.500 Their research demonstrated that magnetic forces can overcome thermal fluctuations in stabilizing particle swarms, particularly for particles with radii of $3\\mu\\mathrm{m}$ . The team observed that a swarm of microparticles could migrate upstream when subjected to combined acoustic and magnetic fields. Moreover, they successfully designed and characterized self-assembled microswarms capable of upstream motility under these conditions. These microswarms exhibited a rolling-type motion along the walls of a microchannel when exposed to external flow and could move upstream against flow velocities up to $1.2\\ \\mathrm{mm}/\\mathrm{s},$ , driven by acoustically induced reaction forces (Figure $27\\mathrm{m}\\mathrm{\\dot{\\Omega}}$ ). The researchers suggest that these bioinspired micro/nanorobotic systems hold promising potential for applications in targeted therapeutics, noninvasive surgery, and precise drug delivery to challenging locations.",
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"chunk": "# 3.4. Optical Actuators \n\nOptical responsive actuators leverage light to induce motion in materials, offering innovative applications in the field of soft robotics. These actuators operate on mechanisms such as photomechanical effects, photochemical reactions, and photothermal changes, which allow materials like azobenzene polymers and liquid-crystal elastomers to expand, contract, or bend in response to specific light wavelengths. Their capacity for precise, remote, and wireless control makes them ideal for scenarios where electrical wiring or complex mechanical setups are impractical. As such, optical responsive actuators are gaining traction in areas such as biomedical devices, adaptive structures, and interactive consumer electronics, where their ability to alter physical properties on-demand can significantly enhance functionality and user interaction. \n\n \nFigure 28. Light-driven liquid-crystal polymer materials actuators. (a) Schematics illustrating the isomeric transformation of azobenzene and the light-driven actuation of liquid-crystal film (LDLCF) (b) Illustration of robot locomotion using a traveling-wave feature, with images showing the displacement of the microrobot as it responds to traveling light patterns of different wavelengths. (c) Schematic of artery wall structure showing the tunica media with alternating muscle and elastic layers for deformation and robustness. Adjacent images show light-induced motion of a silicone oil slug in a TMA, taken through an optical filter to block wavelengths below $530~\\mathrm{nm}$ . (d) Chemical composition of the LC monomer mixture and photographs depicting the rolling motion of a kirigami robot activated by light irradiation. (e) Schematics showing an opto-chemo-mechanical feedback loop modulates the transiently activated region, crucial for complex micropost motion. (f) Schematic diagrams illustrating the mechanism behind artificial goosebump generation in the microactuation system. (a) Reproduced with permission from ref 501. Copyright 2015, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (b) Reproduced with permission from ref 502. Copyright 2016 Springer Nature. (c) Reproduced with permission from ref 503. Copyright 2016 Springer Nature. (d) Reproduced with permission from ref 504. Copyright 2019 Wiley. (e) Reproduced with permission from ref 505. Copyright 2022 Springer Nature. (f) Reproduced with permission from ref 506. Copyright 2024, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. \n\n \nFigure 29. Optical-driven soft actuators based on hydrogels, shape-memory polymers, and light responsive liquids. (a) Schematic illustration showing the expansion and contraction of the $[\\mathrm{c}2]\\mathrm{AzoCD}_{2}$ hydrogel in response to photoirradiation. (b) Schematics of the photoexpansion actuation and photographs of the bending performance. (c) Schematics of the supramolecular photoresponsive hydrogel and photograph of the twisted hydrogel. (d) Schematic depicting the design of light-induced bidirectional contraction−expansion. (e) Illustration of the chemical structure photopolymerizable photo switching monomer and the light-responsive walking of the hydrogel hybrid under rotating magnetic fields. (f) Illustration of the photoisomerization of dithienylethene (DTE) and the gel−sol transition process. (g) Shape-memory polymer ribbon exhibiting spiral shape recovery to its original form under UV illumination. (h) Images showing the shape-memory performance of the hyperbranched coumarate polyesters-based polymer film responding to UV light. (i) Photographs capturing the light-driven motion of an olive oil droplet on a silica plate (j) Illustration of the manipulation of droplets in three dimensions. (a) Reproduced with permission from ref 507. Copyright 2016 Springer Nature. (b) Reproduced with permission from ref 508. Copyright 2020 American Chemical Society. (c) Reproduced with permission from ref 509. Copyright 2020 Wiley. (d) Reproduced with permission from ref 510. Copyright 2024, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (e) Reproduced with permission from ref 511. Copyright 2020 American Association for the Advancement of Science. (f) Reproduced with permission from ref 512. Copyright 2020 Wiley. (g) Reproduced with permission from ref 513. Copyright 2005 Springer Nature. (h) Reproduced with permission from ref 514. Copyright 2013 Wiley. (i) Reproduced with permission from ref 515. Copyright 2000 American Association for the Advancement of Science. (j) Reproduced with permission from ref 516. Copyright 2018 Wiley. \n\n3.4.1. Liquid-Crystal Polymers. Liquid-crystal polymer (LCP) materials based optical actuators represent a sophisticated blend of material science and light-responsive technology, providing a dynamic foundation for advancements in soft robotics and beyond. These actuators utilize the unique properties of LCPs\u0001such as their molecular alignment and anisotropy\u0001to respond to light, particularly in the ultraviolet and visible spectra. When light is applied, LCPs can undergo rapid changes in shape, alignment, or both, driven by the material’s tendency to order or disorder its molecular structure in response to photonic stimuli. Huang et al. developed a miniaturized swimming soft robot capable of complex movement, uniquely powered and controlled by remote light signals, thus eliminating the need for onboard electronics or batteries. The design features a head, a flexible flagellum, and a gripper. The flagellum, made from a flexible polymer, is actuated by a light-driven liquid-crystal film (LDLCF) embedded with azobenzene chromophores. Exposure to UV light causes the LDLCF to bend, swinging the flagellum to propel the robot forward, while exposure to visible light allows it to recover its original shape (Figure 28a). The gripper, constructed from LDLCF and polyethylene terephthalate (PET), opens and closes in response to light, enabling it to grasp and release objects. Controlled by alternating flashes of UV and white light, this innovative approach allows the robot to swim and transport loads effectively.501 Fischer’s group used structured light to control the shape changes and locomotion of microrobots made of photoactive liquid-crystal elastomers (Figure 28b). They fabricated cylindrical and disc-shaped microrobots and demonstrated that they can be driven by structured monochromatic light to perform biomimetic motions. The microrobots were able to generate travelingwave motions to self-propel without external forces or torques. The researchers also showed that the microrobots can exhibit versatile locomotion behaviors on demand, including translational motion and rotation, by controlling the light patterns. The use of structured light fields allowed for precise control over the local actuation dynamics within the microrobots, enabling high-level control over their macroscopic behavior.502 Lv et al. conducted a detailed study on the development of linear liquid-crystal polymer (LLCP) fibers and films. They explored the behavior of tubular microactuators (TMAs) crafted from LLCP films, assessing their mechanical robustness and capability for light-induced motion (Figure 28c). Demonstrations included the use of photoinduced asymmetric deformation of TMAs to manipulate fluid slugs, enabling the mixing of multiphase liquids, combining liquids, propelling liquids uphill, and capturing and conveying microspheres.503 \n\nCheng et al. have innovatively applied kirigami-based techniques combined with liquid-crystal polymer networks (LCNs) to craft complex 3D robotic structures activated by light.504 They manipulated the LCN films through external stress fields such as stretching, twisting, and bending to initiate out-of-plane deformations. By engraving kirigami patterns onto these films and then detaching them from the substrate, the team achieved 2D-to-3D shape morphing (Figure 28d). This approach was exemplified in the creation of a kirigami-based rolling robot equipped with light-actuated petals, which demonstrated the ability to perform multigait rolling movements, navigate predesigned 2D trajectories, and climb slopes. This study underscores the potential of kirigami as a versatile technique for developing complex, flexible 3D structures with light-activated robotic functionalities. Aizenberg’s group has advanced the use of liquid-crystalline elastomers (LCEs) to create microstructures that demonstrate self-regulated actuation under various conditions (Figure 28e).505 They utilized finite element simulations and developed a discrete model to study the collective behavior of microstructure arrays. The research showed that LCE microstructures could perform stroke-like motions under intense illumination, with the trajectories of these motions influenced by factors like illumination intensity, director tilt, size, temperature, and irradiation patterns. Further exploration revealed that microstructures with complex geometries and multiple joints could achieve programmable deformations and intricate motion patterns. Sitti’s group has developed a microactuation system utilizing light-responsive liquid-crystal elastomers (LCEs) as artificial skin, coupled with 3D-printed passive polymer microstructures.506 This innovative system is activated through exposure to a programmable femtosecond laser, which precisely generates localized artificial goosebumps (Figure 28f). This capability enables controlled manipulation of light reflection, disassembly of self-assembled microstructures held together by capillary forces, and has potential applications in information storage. The fabrication of this system involves a two-step thiol-Michael reaction that incorporates mobile liquid-crystal molecules, enhancing the responsiveness of the LCE skin. The system has been further applied to create micromirrors and selectively open and close mushroom-like microstructures, demonstrating its versatility. \n\n3.4.2. Hydrogels. Hydrogel actuators are emerging as a transformative technology in the field of soft robotics, leveraging the unique properties of hydrogels\u0001water-swollen polymeric networks capable of undergoing significant volume change in response to various stimuli. These actuators harness the inherent softness, flexibility, and high-water content of hydrogels, making them ideal for mimicking the natural movements of living tissues, thus offering biocompatibility and inherent safety for interactions with humans. The primary appeal of hydrogel actuators lies in their ability to respond to a range of environmental cues such as temperature, pH, light, and electric or magnetic fields. This responsiveness can induce rapid and reversible changes in the hydrogel’s shape, volume, and mechanical properties, driving the actuation needed for movement and function in soft robotic systems. For instance, temperature-responsive hydrogels can expand or contract substantially with slight changes in temperature, enabling actuation that can mimic muscle contractions. \n\nHarada’s group has innovated in the development of photoresponsive molecular actuators by utilizing rotaxanebased compounds, specifically [c2]daisy chains.507 These actuators were synthesized through the cross-linking of a cyclodextrin (CD) derivative and TetraPEG via amide bond formation. The resulting $[\\mathrm{c}2]\\mathrm{AzoCD}_{2}$ hydrogel and xerogel demonstrated remarkably fast response times to ultraviolet irradiation, with the xerogel responding 10,800 times faster than its hydrogel counterpart (Figure 29a). Notably, the [c2]AzoCD2 xerogel also exhibited pseudoreversible deformation under dry conditions. Stupp’s group has developed innovative cross-linked hydrogel networks by synthesizing polymerizable sulfonated spiropyran molecules (Figure 29b). Unlike typical photoresponsive materials that contract, these hydrogels exhibit a unique expansion upon exposure to visible light. This photoexpansion effect can be finely adjusted by altering the solution’s $\\mathrm{\\ttpH}$ or the composition of the polymeric backbone. Furthermore, the researchers engineered artificial muscles demonstrating negative phototaxis\u0001bending away from the light source.508 The bending angle and the kinetics of these artificial muscles can be tailored through strategic selection of spiropyran molecules and polymeric networks. However, light-driven hydrogel actuators face significant hurdles, notably sluggish responsiveness and subpar mechanical characteristics. A novel approach has emerged to tackle these challenges. Connal’s group pioneers a novel supramolecular design strategy, harnessing a benzylimine-functionalized anthracene group. This innovation not only shifts the absorption spectrum into the visible range but also bolsters the supramolecular network via π−π interactions (Figure 29c).509 \n\nMoreover, the integration of acid−ether hydrogen bonds serves to dissipate energy during mechanical stress, while preserving the hydrogel’s hydrophilicity. The resultant doublecross-linked supramolecular hydrogel, synthesized with ease, boasts a unique combination of superior strength, rapid selfrepair, and swift shape transformation under visible light stimuli, whether in wet or dry conditions. Prior endeavors predominantly focused on either intricately designing heterogeneously structured hydrogels or intricately manipulating external stimuli. However, achieving self-regulated reversal shape deformation in homogeneous hydrogels under a constant stimulus posed a considerable challenge. Guo and colleagues introduce a molecularly designed homogeneous hydrogel containing two spiropyrans that demonstrate selfregulated transient deformation reversal under constant illumination.510 They further developed hydrogel film actuators capable of intricate temporary bidirectional shape transformations and self-regulated reversal rolling under illumination. (Figure 29d). Li et al. engineered hydrogel− metal hybrid materials capable of swift and customizable locomotion when exposed to light and magnetic fields.511 The hydrogels exhibit shape changes in response to both light and magnetic fields. When exposed to light, dehydration caused by the isomerization of a photoswitching molecule leads to the shrinkage of the hydrogels, resulting in mechanical deformation and bending. In the presence of a magnetic field, ferromagnetic nanowires embedded within the hydrogel experience magnetic torques and tend to align with the field, inducing macroscopic deformation. The hydrogels are sensitive to spatial gradients in hydrophobicity triggered by light exposure and the alignment of nanowires induced by the magnetic field, enabling programmable shape changes and locomotion (Figure 29e). These hydrogels exhibited versatile mobility, including walking, steering, climbing, and cargo delivery, all orchestrated by an external magnetic field and light. Zhang’s group introduced a photoresponsive hydrogel− nanopipette hybrid system for single-cell operations, enabling precise drug delivery with minimal cell damage.512 Unlike previous methods, this system operates without high electric potential or organic solvents, preserving cell integrity. Leveraging the hydrogel’s photoresponsive properties, it achieves potential-free, noninvasive drug injection, ensuring high cell viability. The system also allows for dose-controllable drug delivery, demonstrated with reduced lethal doses of doxorubicin across cell lines. The gel−sol transition triggered by visible light drives drug injection without external stimuli (Figure 29f). Real-time control over dosage is achieved through light stimulation. This breakthrough offers a promising tool for single-cell studies and theranostics. \n\n3.4.3. Shape-Memory Polymers. Shape-memory polymer actuators represent a cutting-edge technology in the field of soft robotics. These advanced materials possess the remarkable ability to change shape in response to external stimuli, such as temperature, light, or electrical fields, and subsequently revert to their original shape upon the removal of the stimulus. This unique property makes shape-memory polymer actuators ideal for a wide range of applications in soft robotics, including gripping, locomotion, and morphing structures. By harnessing the inherent flexibility and adaptability of shape-memory polymers, researchers are paving the way for the development of highly versatile and intelligent robotic systems capable of performing complex tasks with precision and efficiency. \n\nLendlein et al. pioneered the light-induced shape-memory polymers, revolutionizing the field with their ability to be deformed and fixed into temporary shapes through UV light illumination (Figure $29\\mathrm{g}$ .513 These polymers exhibit the capability to revert to their original shape when exposed to UV light of a different wavelength. This study involved synthesizing two types of photoresponsive. Additionally, the researchers conducted tests on a specific grafted polymer, investigating the influence of UV irradiation on the temperature of the polymer film during the experiments. Kaneko’s group synthesized hyperbranched coumarate polyesters through a polycondensation method, resulting in polymers with elastomeric properties, excellent solubility, and shape-memory characteristics.514 Furthermore, the researchers explored the photodeformation capabilities of these polymers, showcasing their potential for intricate shape-memory effects (Figure 29h). \n\n3.4.4. Light-Responsive Liquids. Light-responsive liquid actuators represent a dynamic and promising frontier in soft robotics. These innovative materials harness the power of light to trigger shape changes and actuation, offering unique advantages such as rapid response times, precise control, and adaptability. \n\nIchimura et al. present an intriguing study on manipulating the macroscopic motion of liquids on solid surfaces through photoirradiation of a photoisomerizable monolayer. By introducing a calix[4]resorcinarene derivative onto a substrate surface, they demonstrate how asymmetrical photoirradiation induces a gradient in surface free energy, resulting in directional motion of liquid droplets (Figure 29i). The direction and velocity of this motion are controllable by adjusting the direction and intensity gradient of light.515 In addition, Officer’s group has demonstrated a breakthrough in fluidic manipulation by showcasing the ability to precisely move droplets in three dimensions using light (Figure 29j). This feat was achieved through the incorporation of a photoactive material, spiropyran (SP), into the droplets, enabling their movement in any direction within water using simple light sources. The motion of the droplets was driven by a light-induced change in interfacial tension, known as Marangoni flow. Moreover, the researchers illustrated the versatility of this technology by combining a photoactive droplet with another carrying a “cargo,” then moving the resulting larger droplet to a designated “reactor” droplet where the cargo undergoes a chemical reaction.516",
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"chunk": "# 3.5. Thermal Actuators \n\nThermal actuators serve as fundamental components in the rapidly evolving field of soft robotics, offering unique capabilities for precise and adaptable motion. These actuators harness the principles of thermal expansion or contraction to induce mechanical deformation, enabling a wide range of dynamic movements. By leveraging thermal energy as a driving force, researchers are exploring innovative avenues for the development of soft robotic systems capable of performing complex tasks with dexterity and efficiency. \n\n3.5.1. Liquid-Crystal Elastomers. Liquid-crystal elastomers (LCEs) represent a fascinating class of materials with immense potential in the realm of soft robotics. These unique materials combine the properties of both liquid crystals and elastomers, offering a remarkable combination of responsiveness, flexibility, and tunability. LCEs undergo reversible shape changes in response to external stimuli such as temperature, light, or mechanical stress, making them ideal candidates for \n\n \nFigure 30. Thermal-driven actuators based on liquid-crystal elastomers. (a) Illustration depicting the actuation mechanism of the LCE fiber and photographs showcasing the length of LCE microfiber in polydomain, monodomain, and isotropic states. (b) Illustration demonstrating the shapemorphing transition from 1D to 2D of cubic voxels. (c) Schematic of the chemical structure of the main-chain LCE capable of surface alignment, accompanied by photographs illustrating mechanical multistability. (d) Photographs illustrating the restoration of deformed dynamic 3D structures. (e) Photograph showing the multimodal dielectric actuations. (f) Illustration of contrast in stability of isotropization temperature $\\left(\\mathrm{{T_{i}}}\\right)$ achieved through annealing between LC vitrimer networks and other polymer networks and thermal actuation of an aligned xLCE between $160~^{\\circ}\\mathrm{C}$ (isotropic phase) and $140~^{\\circ}\\mathrm{C}$ (liquid-crystal phase) after 200 rapid heating−cooling cycles on a hot plate. (g) LCE shells actuators with negative order parameter. (a) Reproduced with permission from ref 517. Copyright 2021 American Association for the Advancement of Science. (b) Reproduced with permission from ref 518. Copyright 2021, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (c) Reproduced with permission from ref 519. Copyright 2015 American Association for \n\nthe Advancement of Science. (d) Reproduced with permission from ref 520. Copyright 2016 American Chemical Society. (e) Reproduced with permission from ref 521. Copyright 2024 Wiley. (f) Reproduced with permission from ref 522. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (g) Reproduced with permission from ref 523. Copyright 2019, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). \n\nactuation and control in soft robotic systems. Their ability to undergo large deformations while maintaining structural integrity opens up new possibilities for designing soft robots capable of intricate movements and shape-shifting behaviors. As researchers continue to explore the diverse capabilities of LCEs and develop novel fabrication techniques, these materials hold promise for revolutionizing the field of soft robotics by enabling the creation of highly adaptive and versatile robotic systems. \n\nCai’s group has devised an innovative approach for fabricating thin LCE microfibers through electrospinning, unlocking a myriad of possibilities in soft robotics and microfluidics (Figure 30a). These LCE microfibers exhibit impressive actuation capabilities, boasting large strains, rapid response speeds, and high-power densities. Leveraging these unique properties, the group successfully engineered microtweezers, microrobots, and a light-powered microfluidic pump, showcasing the versatility of electrospun LCE microfiber actuators. Notably, the electrospinning technique enables mass production of LCE microfibers with small diameters, resulting in enhanced thermal actuation speeds compared to previous methods.517 In another work, Sitti’s group has pioneered a novel method for fabricating liquid-crystal elastomers (LCEs) with intricate three-dimensional (3D) geometries and customizable director fields.518 Leveraging a two-photon polymerization process, they engineered individual LCE voxels with programmable director fields, allowing precise control over their shape and orientation. These voxels were skillfully assembled to create 1D, 2D, and 3D structures with unprecedented director fields, enabling shape-morphing behaviors previously unattainable (Figure 30b). They showcased the potential for programmable shape transformations in LCE devices, shedding light on their promising applications in diverse fields. White’s group has developed an innovative method for crafting soft, ordered LCEs, capable of programmable shape change and actuation (Figure 30c).519 Their approach involves precisely directing the molecular order, referred to as the director, within small volume elements called voxels. Through this control over the voxel director, they have achieved mastery over the material’s mechanical response. Demonstrating their versatility, these materials exhibit controlled bending and stretching in response to thermal or chemical stimuli. The programmable mechanical behavior of these LCEs holds significant promise for applications ranging from monolithic multifunctional devices to reconfigurable substrates for flexible devices in aerospace, medicine, and consumer goods industries. \n\nJi’s group has introduced a novel material termed carbon nanotube dispersed liquid-crystalline vitrimers, unlocking the potential for crafting dynamic three-dimensional (3D) structures.520 These structures exhibit reversible shape changes and boast easy modification, repair, and assembly facilitated by light activation (Figure 30d). Remarkably, this material enables the fabrication of intricate 3D structures sans the need for screws, glues, or molds. Demonstrating its versatility, the researchers showcased the creation of complex structures with diverse alignment modes. Furthermore, the material’s photowelding capability permits the joining of different parts without adhesives, while its capacity for healing microcracks and functioning at extremely low temperatures further enhances its utility. Jin’s group has introduced a liquid-crystal dielectric elastomer (LC-DE) capable of dynamically altering its dielectric actuation modes in response to temperature fluctuations. Fabricated using liquid-crystal organo-gels, the LC-DE undergoes reversible shape changes with varying temperatures. Notably, the temporary and permanent shapes of the LC-DE possess distinct bending stiffness, resulting in different dielectric actuation modes under an electric field. The temporary shape can be programmed or reprogrammed through force-directed solvent evaporation, while the permanent shape can be reconfigured through bond exchangeenabled stress relaxation. This innovative material exhibits multimodal dielectric actuation behaviors upon temperature change, with the potential for further diversification through shape programming (Figure 30e). Additionally, the LC-DE demonstrates reduced driving electric field requirements and bidirectional actuation manners, attributed to the space charge mechanism. Ji’s group conducted a study into the impact of annealing on the structure and properties of a thermotropic LCE (Figure 30f). Their research revealed that annealing induces changes in the actuation temperature of the LCE. Through a series of experiments, they measured the actuation temperature and utilized differential scanning calorimetry (DSC) to analyze the thermal behavior of the LCE. The study demonstrated that annealing fully cross-linked LCEs with dynamic covalent bonds enables adjustment of the isotropization temperature $(T_{\\mathrm{i}})$ , consequently affecting the actuation temperature. Remarkably, the changes in $T_{\\mathrm{i}}$ were reversible and stable, offering the capability to tune actuation temperatures without altering the material’s chemical composition. Lagerwall’s group has delved into the realm of LCE shell actuators with a negative order parameter through a combination of experiments and simulations. Employing a microfluidic and osmotic stretching approach, they successfully engineered LCE shells exhibiting a negative order parameter. Furthermore, the researchers explored the thermal response of these LCE shells, showcasing various actuation modes (Figure $30{\\bf g})$ ). This study unveils a novel class of LCE actuators with unique characteristics and sheds light on the behavior of liquidcrystal elastomers.523 \n\n3.5.2. Shape-Memory Materials. A shape-memory polymer (SMP) possess the remarkable ability to undergo substantial deformations in response to external stimuli, such as temperature, light, or moisture, and subsequently revert to their original shape upon stimulus removal.524,525 This unique capability enables SMP actuators to execute complex and precise movements, making them ideal candidates for a wide array of applications in soft robotics. By harnessing the inherent flexibility and adaptability of SMPs, researchers are forging new pathways in the development of intelligent and responsive robotic systems capable of performing intricate tasks with agility and efficiency. \n\n \nFigure 31. Thermal-driven actuators based on shape-memory materials. (a) Schematic illustrating the temperature-dependent structures of the double-crystalline SMPs. (b) Illustration of the two-way reversible shape-memory behavior. (c) Photographs illustrating the shape-recovery behaviors of encapsulated composites without onset delay. (d) Optical image depicting a soft electrically actuated quadruped (SEAQ) walking on a rocky surface. (e) Illustration of the object grabbing performance facilitated by the parallel-processable synaptic array (PPSA). (f) Sequential images capturing Tribot in its distance-jump gait, from initial to landing positions. $(\\mathrm{g,h,i})$ Shape-memory ceramic, hydrogel, and hybrid-based actuators. (a) Reproduced with permission from ref 526. Copyright 2021 Elsevier. (b) Reproduced with permission from ref 527. Copyright 2014 American Chemical Society. (c) Reproduced with permission from ref 528. Copyright 2023 Springer Nature. (d) Reproduced with permission from ref 461. Copyright 2018 American Association for the Advancement of Science. (e) Reproduced with permission from ref 529. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (f) Reproduced with permission from ref 530. Copyright 2019 Springer Nature. (g) Reproduced with permission from ref 531. Copyright 2013 American Association for the Advancement of Science. (h) Reproduced with permission from ref 532. Copyright 1995 Springer Nature. (i) Reproduced with permission from ref 533. Copyright 2012 Elsevier. \n\nPan’s group conducted a comprehensive study on doublecrystalline supramolecular polymers (SMPs) comprising oligomeric polycaprolactone (PCL) end-functionalized with self-complementary quadruple hydrogen bonding 2-ureido-4- pyrimidinone (UPy) units. Notably, the study unveiled the dual shape-memory effects of the UPy-terminated PCLs (U \n\nPCLs), showcasing their potential for diverse applications (Figure 31a).526 Sheiko’s group has pioneered a novel strategy for facilitating reversible shape transformation in semicrystalline shape-memory materials.527 This innovative approach integrates three distinct shape-memory behaviors: conventional one-way shape memory, two-way reversible shape memory, and one-way reversible shape memory. The key to achieving shape reversibility lies in the partial melting of a crystalline scaffold, which leaves behind a latent template for recrystallization and ensures the memory of a temporary shape. \n\n \nFigure 32. Chemically driven actuators. (a) Schematic of the actuation mechanism of the porous membrane actuator and photographs of reversible actuation of star-shaped “flower” responding to humidity changes. (b) Schematic showing the configuration and actuation mechanism of the multifunctional soft actuator. (c) Schematic depicting the synthesis of carbon nitride polymer (CNP) composed of heptazine as a repeating unit and illustration showcasing the actuating motions of a CNP film triggered by changes in humidity. (d) Schematic illustration depicting the structure of MXene-Based soft actuator and the mechanism for moisture-driven actuating, humidity energy harvesting, self-powered humidity sensing, and real-time motion tracking. (e) Illustration depicting the mechanism of switchable and reversible biocatalyzed bending of a bilayer hybrid asymmetric hydrogel system. The lower row shows the reversible pH-stimulated bending of the bilayer glucose oxidase/urease-functionalized asymmetric hybrid system responding to glucose or urea. (f) Schematic showing the actuation mechanism of the glucose-powered polymer actuator and photographs showing the actuation performance. $(\\mathbf{g})$ Schematic depicting the actuation process of the urease-containing gel, showcasing the pH-responsive behavior and time-lapse photographs illustrating the dynamic actuation performance of the gel over time. (a) Reproduced with permission from ref 534. Copyright 2014, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (b) Reproduced with permission from ref 535. Copyright 2022 American Chemical Society. (c) Reproduced with permission from ref 536. Copyright 2016 Springer Nature. (d) Reproduced with permission from ref 537. Copyright 2021 American Chemical Society. (e) Reproduced with permission from ref 458. Copyright 2016 American Chemical Society. (f) Reproduced with permission from ref 457. Copyright 2019 Wiley. (g) Reproduced with permission from ref 538. Copyright 2023 Wiley. \n\nThrough experimentation with various shapes, the authors demonstrated the efficacy of this strategy and elucidated the role of polymer crystallites in shaping memory behavior (Figure 31b). \n\nNi et al. developed a shape-memory hydrogel utilizing phase separation as the underlying mechanism.528 This hydrogel, formulated from an aqueous photocurable resin, demonstrated remarkable versatility and programmability. By adjusting the programming time and monomer concentration, they could precisely control the degree of phase separation and the onset delay of shape recovery (Figure 31c). The team showcased the hydrogel’s versatility by fabricating a range of devices with customizable shape recovery properties. Other than shapememory polymers, shape-memory alloys have also been widely used in soft robotics. Majidi’s group has developed untethered soft robots capable of dynamic locomotion at speeds comparable to biological organisms.461 This achievement is realized through the utilization of compliant lightweight actuators featuring a shape-memory alloy (SMA). These SMA-based compliant actuators can seamlessly transition between a compliant unactuated state and a stiff actuated state, enabling rapid motions and generating substantial forces akin to natural muscle. The soft robots were validated in two distinct testbeds: a soft electrically actuated quadruped (SEAQ) and a multigait caterpillar-inspired robot. The SEAQ demonstrated versatile locomotion capabilities, including walking on various surfaces, climbing over obstacles, and achieving a maximum speed of 0.56 body lengths per second (blps) (Figure 31d). Cho’s group has developed a fully parallel-processable control system for robotic fingers by integrating a synaptic array with a robotic hand.529 The synaptic array, composed of ion-gel-based synaptic transistors connected to an ion gel dielectric, enables parallel signal processing and multiactuation control. Meanwhile, the robotic hand comprises three fingers crafted from NiTi shape-memory alloy fiber embedded in a 3D-printed body. The researchers demonstrated that the synaptic control system facilitates coordinated finger movement with reduced control complexity, leveraging the benefits of parallel multiplexing and analog logic. Furthermore, they showcased the system’s capability to execute complex actuations, including grasping objects with curvature and intricate designs (Figure 31e). Paik’s group designed a compact robot inspired by trap-jaw ants, capable of executing five distinct locomotion modes: vertical jumping, horizontal jumping, somersault jumping, walking, and crawling.530 This versatile locomotion mechanism is designed with minimal components and assembly steps, offering tunable power requirements (Figure 31f). In addition, shape-memory ceramic, shape-memory hydrogels, and shape-memory hybrid materials are also used to fabricate actuators (Figure 31g,h,i).531−533",
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"chunk": "# 3.6. Chemical Actuators \n\nChemical actuators represent a unique class of materials utilized in soft robotics, offering distinct advantages in responsiveness, adaptability, and energy efficiency. These actuators harness chemical reactions to induce mechanical motion, enabling precise control and manipulation of soft robotic systems. By leveraging the inherent properties of responsive materials in response to specific environmental stimuli such as $\\mathrm{\\ttpH}_{\\mathrm{\\tt3}}$ , temperature, light, or solvent composition. This brief intro highlights the pivotal role of chemical actuators in soft robotics, paving the way for innovative applications in fields ranging from biomedical devices to adaptive structures and beyond. \n\n3.6.1. Organic Vapors and Solvents. Yuan’s group has introduced a groundbreaking porous polymer actuator that exhibits exceptional responsiveness to acetone vapor, surpassing previous state-of-the-art actuators by an order of magnitude in speed (Figure 32a). This novel actuator stands out for its multiresponsiveness to various organic vapors, both in dry and wet conditions, distinguishing it from conventional gel actuation systems that lose effectiveness when dried out.534 Notably, the actuator is straightforward to manufacture and can endure rigorous processing and pressing treatments. Furthermore, the researchers showcased the transferability of the actuator’s responsiveness to other objects through surface coating. This performance is attributed to the actuator’s unique combination of porous morphology, gradient structure, and the interaction between solvent molecules and the actuator material. Wang’s group has fabricated a multifunctional soft actuator driven by liquid, vapor, and light, leveraging a PDMS/ CNTs-PDMS-PVDF sandwich structure film.535 This innovative actuator demonstrated rapid and robust responses to various organic solvents, vapor, and light stimuli. Its fast response speeds and ability to mimic diverse motion behaviors make it promising for applications in energy-saving soft grippers, fast-speed soft crawlers, and jellyfish-like soft swimmers (Figure 32b). Additionally, the group developed another sandwich actuator driven by multiple external stimuli, including liquids, vapor, and solar light, utilizing aligned carbon nanotubes. This ultrafast-responsive actuator supports programmable motions and holds potential applications in healthcare, bioengineering, chip technology, and mobile sensors, showcasing its versatility and broad utility across various fields. \n\n3.6.2. Humidity-Related Reactions. Aida’s group has made significant strides in developing an autonomous film actuator responsive to ambient humidity fluctuations. Crafted from a $\\pi$ -stacked carbon nitride polymer, this film boasts a tough, ultralightweight, and highly anisotropic layered structure (Figure 32c). Remarkably, the actuation of the film is rapid and durable, capable of over 10,000 repeated cycles without degradation.536 The researchers further showcase the film’s ability to unidirectionally walk when shielded from water adsorption. Its actuation is primarily driven by the adsorption and desorption of minute water amounts triggered by ambient humidity shifts. Additionally, the film can be activated by either heating or light irradiation. \n\nQiu’s group has developed a moisture-responsive actuator using MXene materials, exploring its properties and applications in generating electricity from ambient humidity. The core of their work lies in a soft actuator, composed of MXene, cellulose, and polystyrene sulfonic acid (PSSA), capable of humidity-driven actuation, energy harvesting, selfpowered sensing, and real-time motion tracking (Figure 32d).537 By capturing the chemical potential of humidity, the actuator generates mechanical power through asymmetric expansion and electricity via directional proton diffusion, offering high power density and open-circuit voltage. \n\n3.6.3. Enzymes. Willner’s group has introduced asymmetric two-layer hybrid DNA-based hydrogels capable of reversible shape transitions.458 These hydrogels feature layerselective switchable stimuli-responsive elements, dictating their stiffness. Trigger-induced stress in one layer induces bending of the hydrogel structure, which can be restored to its original linear bilayer form upon stress removal. The stiffness of the DNA hydrogel layers can be controlled by various triggers, including thermal, pH, $\\mathrm{K^{+}}$ ion/crown ether, chemical, or biocatalytic stimuli (Figure 32e). Jager’s group has introduced a self-powered artificial muscle fueled by glucose and oxygen .457 This innovative actuator integrates glucose oxidase and laccase enzymes, which catalytically convert glucose and oxygen into electrical power. The generated electrical energy drives movement in the actuator, facilitated by the electroactive polymer polypyrrole, resulting in bending motions (Figure 32f). The integrated bioelectrode pair exhibits a maximum open-circuit voltage of $0.70~\\pm~0.04~\\mathrm{~V~}$ and a maximum power density of $0.27\\mu\\mathrm{W}\\ \\mathrm{cm}^{-2}$ . Through full integration of the enzymes, the artificial muscle operates autonomously, capable of reversible bending in both directions solely powered by glucose and oxygen. This advancement holds promise for applications in soft robotics, implantable medical devices, and environmental monitoring. \n\n \nFigure 33. Soft robots by other actuation modalities. (a) Diagram depicting the ultrasound-driven actuator. When subjected to sweeping frequency ultrasound excitation, the artificial muscle undergoes multimode deformation over time, as illustrated at time points $\\mathrm{T}_{1},\\mathrm{T}_{2},$ and $\\mathrm{T}_{3}$ . (b) Optical images depicting the downward motion of microrobots within 3D channels inclined at different angles. (c) Photographs demonstrating the precise modulation of the Venus flytrap’s response time, enabling the phytoactuator mounted on a manipulator to capture a moving object. (d) Photographs displaying muscular bending responses of the worm under different laser intensities. (e) Images showing the pillar couple bends when wet and straightens when dry (top). (f) Bioinspired design of the autonomous seed carrier with self-drilling capability. $(\\mathbf{g})$ Image depicting the engineered jumper alongside time-lapse frames capturing the acceleration phase of the jumper’s motion. (h) Structural depiction and operational principle of the phase-change-based soft composite material, alongside an illustration demonstrating the expansion process using a single ethanol bubble. (i) Schematic illustrating the mechanism of combustion and time-lapse images showcasing the actuation process. (a) Reproduced with permission from ref 540. Copyright bioRxiv 2024, under a CC-BY-NC-ND 4.0 International license. (b) Reproduced with permission from ref 541. Copyright 2023, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). (c) Reproduced with permission from ref 464. Copyright 2021 Springer Nature. (d) Reproduced with permission from ref 543. Copyright 2021 American Association for the Advancement of Science. (e) Reproduced with permission from ref 544. Copyright 2022 Springer Nature. (f) Reproduced with permission from ref 545. Copyright 2023 Springer Nature. $\\mathbf{\\eta}(\\mathbf{g})$ Reproduced with permission from ref 546. Copyright 2022 Springer Nature. (h) Reproduced with permission from ref 547. Copyright 2017, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (i) Reproduced with permission from ref 548. Copyright 2023 American Association for the Advancement of Science. \n\n3.6.4. pH. Walther’s group presented a method to achieve autonomous control in soft robotic actuators by integrating autonomous chemical controllers with pH-responsive hydrogels.538 Leveraging a one-component transient pH-flip mechanism based on activated carboxylic acids, which undergo spontaneous $\\mathrm{\\tt{pH}}$ -dependent decarboxylation in solution, they demonstrate precise control over $\\mathrm{\\ttpH}$ drop and transient state duration by injecting varying quantities of the activated acid into a buffer solution (Figure $32\\mathrm{g}$ . By coupling this pH-flip with hydrogels, they enable autonomous motion, leveraging the hydrogels’ swelling and deswelling responses to $\\mathsf{p H}$ changes for actuation. Their approach finds applications in interlocking puzzle pieces and grasping objects with small orifices. Furthermore, they explore incorporating chemomechanical feedback by coupling the pH-flip with the urea/ urease enzymatic reaction, facilitating additional control over actuation.",
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"chunk": "# 3.7. Other Actuation Modalities \n\nIn addition to the aforementioned actuation modalities for soft actuators, there are other types such as acoustic, biohybrid, and combustion. Moreover, combining multiple actuation modalities within a single soft robot is not uncommon.539 There are still many innovative actuation modalities yet to be discovered and exploited. \n\n3.7.1. Acoustic. Ahmed’s group has introduced a new class of soft artificial muscles that leverage acoustically activated microbubble arrays to achieve precise and programmable actuation (Figure 33a). These acoustic artificial muscles are characterized by their dynamic programmability, large force intensity, rapid responsiveness, wireless controllability, all while being exceptionally compact and lightweight. Their findings demonstrate that the use of resonant microbubbles allows for the amplification of acoustic energy, enabling a weak sound source to produce substantial actuation in the artificial muscle. They have successfully engineered a broad array of preprogrammed movements and applications for the artificial muscle, which are directed by the unique configuration of the microbubbles and the ultrasound excitation parameters, such as voltage and frequency. They showcased the strength and durability of these muscles by incorporating the variable-sized microbubble arrays into devices such as a soft gripper, a biomimetic stingray robot, a shape transformer, and a robotic skin.540 In another study, they developed an acoustically driven helical microrobot that emulates the spiral motion of natural microswimmers (Figure 33b). Fabricated using 3D printing, these microrobots were meticulously examined through computer simulations to ascertain the forces and torques acting upon them. Through manipulation with a single sound source, the microrobots showcased bidirectional movement, with the ability to adjust motion by modulating the acoustic frequency.541 \n\n3.7.2. Biohybrid. Biohybrid robots are another interesting and promising type of robots.542 Chen’s group developed an electrical phytoactuator by pairing a Venus flytrap as the actuating component with conformable electrodes for modulation.464 They engineered plant-conformable electrodes and adhesive hydrogels to interact with the plant without impeding its movement or physiology. This phytoactuator could be wirelessly controlled via smartphone and integrated into diverse platforms. The study showcased its capability to grasp thin wires and capture moving objects, boasting a rapid response time of approximately 1.3 s and low power consumption (Figure 33c). Additionally, the research delved into the design, fabrication, and characterization of the system’s components, outlining potential applications and future avenues of exploration. Liu’s group provided a method to regulate the locomotion of a live Caenorhabditis elegans worm, culminating in the creation of a controllable living soft microrobot named “RoboWorm” via optogenetic excitation and visual feedback. The RoboWorm successfully emulated natural worm locomotion patterns and adeptly maneuvered through obstacles (Figure 33d). The study also entailed the development of dynamic and kinematic models to elucidate the worm’s crawling mechanism and the implementation of closed-loop motion control.543 \n\n3.7.3. Humidity. Zhang et al. conducted extensive experiments and analyses to explore the hygroscopic motion of pine cones and develop biomimetic actuators based on this mechanism.544 They investigated the microstructure of vascular bundles (VBs) within the pine cones and analyzed their role in hygroscopic deformation. Building on these findings, they 3D printed artificial actuators mimicking the pine cone structure and demonstrated their mechanical properties and shape transformations. The study revealed that pine cones undergo ultraslow deformation primarily due to the unique spring/square microtube heterostructure of the VBs. Inspired by this mechanism, the researchers developed soft actuators capable of controlled, imperceptibly slow motion (Figure 33e). These actuators have potential applications in camouflage and reconnaissance due to their low motion velocity compared to other humidity-driven actuators. Yao et al. devised and manufactured autonomous self-drilling seed carriers utilizing wood veneer as the primary material. Drawing inspiration from the hygroscopic behavior of natural grass seeds, the carriers featured a three-tailed configuration and a hygromorphic coiling body (Figure 33f). Tailoring the design for diverse terrains and payloads, including embedding symbiotic species and delivering beneficial nematodes, the seed carriers exhibited promising applications in agriculture, reforestation, and environmental conservation. Additionally, the study elucidated design principles for transforming wood veneer into stiff and biodegradable hygromorphic actuators with substantial bending curvatures.545 \n\n3.7.4. Energy Storage. Hawkes et al. presented a comparative model examining the energetics of biological and engineered jumpers.546 They evaluated specific-energy production limits and utilization in both biological and engineered jumpers, analyzing components like motor, spring, linkage, and payload. The findings revealed that while biological jumpers are constrained by the work capacity of linear muscles, engineered jumpers can surpass this limit by employing work multiplication. Leveraging these insights, the researched devised an engineered jumper featuring a highspecific-energy hybrid spring-linkage, achieving remarkable jump heights exceeding $30\\mathrm{~m~}$ (Figure $33\\mathbf{g}$ ). \n\n3.7.5. Phase Change. Lipson’s group pioneered a selfcontained soft composite material featuring a silicone elastomer matrix infused with ethanol distributed in microbubbles (Figure 33h). This innovative material boasted impressive mechanical properties, including high strain and stress capabilities coupled with low density. It offered versatility in manufacturing methods, allowing for casting or 3D printing, and found utility as an actuator in diverse robotic applications. Demonstrating remarkable expansion−contraction abilities, it showcased the capacity to lift weights exceeding its own. Notably, it provided high strain levels even when powered by low-voltage sources, rendering it ideal for untethered applications.547 \n\n \nFigure 34. A summary of future development of soft actuators. (a) Key points for the significance and future development for several performance indicators of soft actuators, including elastic modulus, actuation strain, work density, power density and strain rate. (b) Comparison of human skeletal muscles with multiple actuators as artificial muscles in terms of the above-mentioned performance indicators. The data was extracted from refs 108, 444, 461, 471, 549−554. \n\n3.7.6. Combustion. Shepherd’s team has pioneered the development of potent soft combustion actuators tailored for insect-scale robots (Figure 33i).548 By leveraging high-energy density chemical fuels, they significantly enhanced the performance of microactuators compared to existing technologies. Demonstrating their innovation, they engineered a $325{\\cdot}\\mathrm{mg}$ soft combustion microactuator capable of remarkable displacements of $140\\%$ , operating frequencies exceeding 100 $\\mathbf{Hz},$ and generating forces surpassing $9.5\\ \\mathrm{N}$ . These advancements were further integrated into an insect-scale quadrupedal robot, showcasing diverse gait patterns, precise directional control, and a payload capacity 22 times its body weight. This robotic platform adeptly navigated uneven terrain and overcame obstacles with ease.",
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"chunk": "# 3.8. Future Development \n\nSoft actuators, designed to emulate the flexibility and functionality of natural muscle, have become pivotal in advancing applications where interaction with humans or sensitive environments is necessary. These actuators offer significant advantages over traditional rigid actuators by providing greater compliance and safety. Among the types explored are pneumatic actuators, known for their high specific power, which can reach up to $20,000~\\mathrm{W/kg};$ shape-memory alloys, which offer distinct responses under thermal activation; dielectric elastomer actuators, which can achieve substantial actuation strains up to $1000\\%$ ; and ionic polymer−metal composites, valued for their low voltage operations. By comparing these with human skeletal muscles, which typically operate within a specific power range of 50 to $280~\\mathrm{W/kg}$ and an actuation strain of $30{-}40\\%$ , it becomes clear how each soft actuator can be optimized for specific tasks in robotics, medical devices, and more (Figure 34). \n\n \nFigure 35. Soft sensing and actuation structures based on buckling. Soft structures in sensing devices, including (a) wavy, (b) crumpling, (c) serpentine, (d) arc-shaped, (e) fractal design, (f) noncoplanar serpentines, (g) 2D spiral, (h) 3D helical, and (i) hierarchical buckling. Reproduced with permission from ref 593. Copyright 2006 Springer Nature. Reproduced with permission from ref 597. Copyright 2013 Springer Nature. Reproduced with permission from ref 53. Copyright 2011 American Association for the Advancement of Science. Reproduced with permission from ref 601. Copyright 2009 Wiley. Reproduced with permission from ref 603. Copyright 2014 Springer Nature. Reproduced with permission from ref 604. Copyright 2008 United States National Academy of Sciences. Reproduced with permission from ref 605. Copyright 2015 IOP publishing. Reproduced with permission from ref 606. Copyright 2017 Springer Nature. Reproduced with permission from ref 607. Copyright 2017 Elsevier. Soft structures in soft robots, including (j) wavy ring, (k) caterpillar structure, (l) spiral shape, $(\\mathtt{m},\\mathtt{n})$ tortuous structures, $(\\circ,\\mathsf{p},\\mathsf{q})$ helical structures. Reproduced with permission from ref 608. Copyright 2023 Wiley. Reproduced with permission from ref 609. Copyright 2023 American Association for the Advancement of Science. Reproduced with permission from ref 610. Copyright 2023 Wiley. Reproduced with permission from ref 611. Copyright 2022 United States National Academy of Sciences. Reproduced with permission from ref 612. Copyright 2021 Springer Nature. Reproduced with permission from ref 613. Copyright 2021 Springer Nature. Reproduced with permission from ref 614. Copyright 2023 American Association for the Advancement of Science. Reproduced with permission from ref 615. Copyright 2014 American Chemical Society. \n\nWhile these artificial muscles often surpass human muscles in specific power and strain capabilities, they also face challenges such as fatigue, the need for external power sources, and sensitivity to environmental conditions, factors where natural muscles generally maintain robust performance. This examination not only delves into the technical specifications of each actuator type but also highlights their transformative potential in the burgeoning field of soft robotics, along with a realistic assessment of their advantages and drawbacks compared to human musculature.",
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"chunk": "# 4. STRUCTURE AND MECHANICS \n\nThe rapid advancements of flexible electronic devices and soft robot have witnessed the transformation from hard and rigid materials-based dyestveicmes. $60,61,96,11\\bar{7},555-5\\bar{6}3$ m t s fwt oarnthd nleoxtiibnlge that the performance of such flexible systems is comparable or even superior than that of conventional rigid devices. For example, the flexibility and biocompatibility of soft neural device could make more precise neural recording possi$\\mathbf{\\Delta}\\mathbf{b}\\mathbf{le},^{564,565}$ and the intrinsic softness of soft robot can increase the safety between the human−robot interaction.566−568 This materials-based paradigm shift opens new windows for boundless applications in prosthetics ,569 brain−machine interface,570,571 metaverse,572 and so on.97,573 However, the employment of soft materials in soft electronics and robots may also suffer a certain number of limitations that could impeded the further real-world applications, such as limited stretchability and hysteresis in soft electronics, low actuation speed and output forces for soft machines, etc.117,574−576 To overcome these challenges, structural materials would provide an excellent solution to the issues.577−580 Assisted by mechanically guided design and analysis, the structural devices could exhibit unprecedent mechanical performances that are hard to achieve by their soft counterparts, achieving ultralow hysteresis and hyperstretchability for electronic devices and fast response time and other elegant performances.581−584 In this section, four types of structures for soft electronics and robots are summarized and discussed: buckling structures, kirigami and origami, fibers and fabrics, and other structures.",
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"chunk": "# 4.1. Buckling Structures \n\nBuckling structures refer to the design or arrangement within the device that allows it to undergo controlled collapsing, crumpling, curving, wrapping, or other behaviors with response to mechanical stress.585−590 This controlled buckling can be utilized to enhance the device’s mechanical properties, such as flexibility, stretchability, or conformability .183,591,592 Moreover, this structure is not uncommon both in electronic devices and soft robotic systems. \n\nIn flexible electronic devices, common buckling structures include wavy, crumpling, serpentine, arc-shaped, fractal design, noncoplanar serpentines, 2D spiral, 3D helical, and hierarchical buckling, as shown in Figure 35a-i. According to mechanics of materials, the rigidity can be greatly reduced by decreasing the thickness of materials, which becomes a widely used approach to fabricate wavy structure for stretchable electronics. By utilizing this approach, precisely engineered wavy semiconductor nanoribbons (GaAs and Si) were successfully prepared by Sun et al.593 This structure can effectively enhance the stretchability of brittle and rigid materials. Moreover, the layout of wavy structure can be precisely controlled by selectively bonding of ribbon thin film and the substrate, on which a number of interesting working are based.594,595 Crumpling is another structural strategy to control the properties of materials, such as graphene.596 Based on this, Zang et al. reported an reversibly controlled approach for the crumpling and unfolding of graphene sheets.597 Serpentine interconnects refer to a specific type of pathway designed to accommodate mechanical strain and deformation. Generally speaking, there are two curved sections (arc) and three linear sections (arm) involved in such serpentine pathway and its mechanical performance is dependent on the parameters of each section, such as the radius and width of arc, and the length of the arm.598−600 In an integrated epidermal electronic system, such serpentine structure is employed for the mechanical performance of the system.53 Like the wavy structure created by selective bonding, the arc-shaped interconnections undergo compression-induced buckling, transitioning from a flat arrangement. As shown in Figure 35d, 2D silicon-based circuits can be engineered to adapt to a wide range of curvilinear surfaces by the employment of structured silicon layouts with polymer/metal interconnects.601 \n\nIn this way, curvilinear electronic devices on arbitrary surface can be well fabricated. Fractal design is a design principle where the same pattern repeats at different scales within a specific structure.602 These designs often exhibit self-similarity, meaning that they look similar at any level of magnification. This principle could provide a powerful design source for both stretchable interconnects and functional layers to achieve large stretchability as well as seamless hard−soft materials integration (Figure 35e).603 \n\nDifferent from the aforementioned serpentine structures, Kim et al. reported the concepts and exploration of noncoplanar structures that could accomplish even high stretchability and accommodate almost every mechanical deformation to high levels of strain (Figure 35f).604 Other structures have also been widely used in electronic devices, such as 2D spiral and 3D helical structure, as shown in Figure 35g, h.605,606 The origin of which can be found in nature and daily objects such as nautilus shells and springs. While the former structures are fabricated by wrapping a circle around a fixed point, the latter one takes the advantage of less physical coupling with the substrate to suppress strain concentrations during the deformation. Besides, Huang et al. reported a new type of self-similar piezoelectric nano/microfibers with hyper stretchability by regulating the manufacture parameters in the electrohydrodynamical printing process, providing insights into the digital manufacturing of stretchable devices (Figure 35i).607 \n\nAs for soft robots, a variety of buckling structures have also been widely used in this field. For example, Zhao et al. reported a self-sustained snapping autonomous soft robot that can be driven either by light or thermal (Figure 35j).608 With wavy ring-like structure, such robot is made of liquid-crystal elastomer and takes the advantage the symmetry of the shape to control the movement pattern of the robot. Specifically, such robots with symmetric shape tend to have self-dancing motion, and those with unsymmetric shapes may exhibit directional crawling behavior due to the friction difference. In another work, a caterpillar-inspired crawling robot with multiple movement modes is developed by Wu et al., as shown in Figure 35k.609 Enabled by joule heating, bidirectional locomotion can also be achieved for this robot due to the friction competition of the front and rear part with the ground. However, due to the intrinsic properties of soft materials, soft robots often suffer from limited agilities. Such limitations can be overcome by structural engineering of the physical body of soft robots. In Figure 35l, a spiral robot made of piezoelectric composites is demonstrated, with an impressive forward locomotion speed of 76 body length per second.6 Apart from locomotion, the ability of grasping is another essentially vital skills for soft robots. Although a number of pneumatic driven soft grippers for grasping tasks have been discussed previously, the requirement of perception and feedback control makes dexterous manipulation and handling much more complicated and challenging. Alternatively, Becker et al. introduced a novel grasping strategy by active entanglement, which circumvent the employment of motion planning and feedback (Figure $35\\mathrm{m}{\\ddot{}}$ ).611 Benefitted from such grasping strategy, a wide range of objects with different size, weight, and shape can be conformably grasped without any sensing and feedback control. Generally speaking, the morphing pattern of soft grippers is closely related with its physical information, such as size, Young’s modulus, and geometry. To make soft robots work effectively as designed, complicated prototyping processes are needed, ranging from modeling to optimization and fabrication. What makes it even worse is that conventional fabrication techniques may limited in terms of scalability, robustness, and design flexibility. To solve this, Jones et al. developed an all-in-one approach to fabricate programmable soft pneumatic actuators by harnessing the interfacial flows in uncured soft materials, as shown in Figure $35\\mathrm{n}$ .612 This novel approach would accelerate the development of soft machines with more complicated structures and provide more functions in soft robot that are attributed to the geometric and material properties. Helical structure is another widely studied designs for soft machines. In two separate works, Hu et al. reported helical-artificial fibrous muscle structured soft actuators with multiple degrees of freedom, with one exhibiting a myriad of oscillating modalities, and another adaptive omnidirectional reorientation abilities, as shown in Figure 35o,p.613,614 On the other hand, helical designs can also be used for propelling robots. Schamel et al. demonstrated the effective control of propulsion of microrobots in complex viscoelastic media, which is promising for future biomedical applications (Figure 35q).615",
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"chunk": "# 4.2. Kirigami and Origami \n\nOriginating form artistic work, Kirigami and origami have a much longer history than soft electronic devices and robotic systems. In their design, a range of techniques are utilized to engineer 2D sheets, such as papers and plastic films, into various 3D structures with special functionalities. Such interesting structures has also provided endless inspiration sources for the development of both flexible sensing devices and soft robotics.616−618 For example, kirigami and origami is not only an approach to design and fabrication of devices with unprecedented flexibility and stretchability, but also provides additional space for the functionality of these device in terms of morphing abilities, signal processing, logic computation, and so on.619,620 This could be an important aspect for next generation flexible systems with intelligence.621 \n\n \nFigure 36. Kirigami and origami structures for sensing devices and actuation. (a) Engineering of elasticity in nanocomposites by kirigami strategy. Reproduced with permission from ref 622. Copyright 2015 Springer Nature. (b) Schematic illustration of snakeskin-inspired kirigami metamaterials for conformal electronic armor. Reproduced with permission from ref 623. Copyright 2022 Wiley. (c) Photograph of the graphene kirigami. Reproduced with permission from ref 624. Copyright 2015 Springer Nature. (d) Photograph of the supercapacitor with customizable stretchability enabled by kirigami. Reproduced with permission from ref 625. Copyright 2018 Wiley. (e) Crawling soft actuator enabled by kirigami skin. Reproduced with permission from ref 626. Copyright 2018 American Association for the Advancement of Science. (f) Kirigami soft grippers. Reproduced with permission from ref 627. Copyright 2021 American Association for the Advancement of Science. (g) Programmable shapemorphing kirigami sheets. Reproduced with permission from ref 628. Copyright 2022 Springer Nature. (h) Programmable shapes by kirigami tessellations. Reproduced with permission from ref 629. Copyright 2019 Springer Nature. (i) Hemispherical electronic eye enabled by origami silicon. Reproduced with permission from ref 630. Copyright 2017 Springer Nature. (j) Origami mechanologic. Reproduced with permission from ref 631. Copyright 2018 United States National Academy of Sciences. (k) Origami paper photodetector arrays. Reproduced with permission from ref 632. Copyright 2017 American Chemical Society. (l) Miura-origami-inspired power generator. Reproduced with permission from ref 633. Copyright 2020 Springer Nature. (m) Shape morphing structures by origami. Reproduced with permission from ref 634. Copyright 2023 Springer Nature. (n) Paper robots integrated based on origami. Reproduced with permission from ref 635. Copyright 2023 Springer Nature. (o) Stretchable origami robotic arm with omnidirectional bending and twisting. Reproduced with permission from ref 636. Copyright 2021 United States National Academy of Sciences. (p) Robotic metamorphosis by origami exoskeletons. Reproduced with permission from ref 637. Copyright 2017 American Association for the Advancement of Science. \n\nElasticity in rigid composite is usually hard to predict due to the stochastic emergence and distribution of strain-concentrating defects. Kirigami could provide a solution to this situation by preventing unpredictable local failure, and the maximum strain can also be increased from $4\\%$ to $370\\%$ , as shown in Figure 36a.622 This predictable behavior of elasticity in nanocomposites and stretchability pave way for the development of stretchable electronic and optoelectronic devices and other applications. From another aspect, Jiang et al. reported a snakeskin-inspired kirigami structure that can be used for adaptive conformal electronics (Figure 36b).623 This structure not only has similar functions as conventional e-skins, but more fascinatingly, could protect itself from external damages. Aside from this, kirigami can also be applied to other materials and dimensions. In their work, Blees et al. demonstrated the kirigami of graphene with tunable and robust mechanical performances in microscale (Figure 36c).624 More fundamentally, they found that the Föppl-von Kármán number in the materials, an indicator of the ratio between inplane stiffness and out-of-plane bending stiffness, is a crucial parameter for kirigami. Materials with higher Föppl-von Kármán number tend to be easier bend and crumple. These insights into graphene kirigami successfully establishes the connection between graphene sheets and microscale resilient systems. Other electronic parts, such as capacitors, can also be empower by the employment of kirigami. Lv et al. developed an editable, stretchable supercapacitors based on mechanically strengthened ultralong $\\mathrm{MnO}_{2}$ nanowire composites, as shown in Figure 36d.625 Move further, such kirigami-based supercapacitors were integrated with strain sensor into a system, and maintained stable sensing capabilities under large deformation, indicating the numerous possibilities of kirigami structures in different kinds of electronic devices. \n\nApart from electronic part, kirigami structures can also find their major roles in soft actuators by providing various types of actuation mechanisms. By harnessing kirigami structures around an extending soft actuator, the crawling abilities of such actuators can be effectively enhanced, as shown in Figure 36e.626 The kirigami structures involved in this crawling robot were induced by the transformation of flat surface to snakeskin-like 3D textured surface. This transformation can further result in the friction force between the surface and the ground and then makes impact on the locomotion. Kirigami can also be used for soft grippers to handling objects that are challenging for conventional grippers. In one work, Yang and co-workers reported a soft gripper using kirigami shells, as shown in Figure $36\\mathrm{f.}^{627}$ Followed by finite element analysis, theoretical modeling, and experiment, the kirigami gripper is proven to grasp delicate and slippery objects. Moreover, such technique to fabricate robotic grippers can be miniaturized, modularized, and remotely actuated, which will promote the development of novel grippers for robotic applications. In another work, Hong et al. also demonstrated a soft gripper based on shape-morphing kirigami sheet (Figure $36\\mathrm{g}$ ).628 The principle behind was based on controlling the curvature of cut boundaries, which was inspired by the Gauss-Bonnet theorem. This programmable, universal, and nondestructive gripper moves further and can grasp even more delicate objects, such as raw egg yolk and human hair. Owning to their merits ranging from rich and compact morphing shape to special materials properties, kirigami tessellations could also be promising for robotics. However, the design of such structures has long been hindered by challenges from geometric and topological constraints. Choi et al. provided a theoretical framework of designing kirigami tessellation structures that can conform to any target shapes and also fabricated corresponding models to validate their inverse design approach (Figure 36h).629 \n\nDifferent from kirigami that cuts papers in a variety of techniques, origami employs folding approaches to engineer the structure and mechanical properties of sheets, which has also been widely reported in soft electronic devices and robotic systems. The first example of origami on soft electronic device is hemispherical electronic eye systems. It is widely known that design and integration of silicon optoelectronic device in hemispherical shape with high resolution is formidable. Based on convex isogonal polyhedral concepts, Zhang et al. successfully fabricated such device on a semispherical surface by shaping the raw silicon-based device into truncated icosahedron and then folding then into hemispherical surfaces (Figure 36i).630 Conventional, the decision process for soft robot is achieved by central processing in the sense-decideaction loop. However, central processing unit is typically rigid and hard, with is incompatible with the soft body of robots. By utilizing an origami waterbomb as a mechanical storage device, Treml and co-workers demonstrated programmable mechanical computation that is embedded into the body of soft robots, as illustrated in Figure 36j.631 Except for electronics device on curved surface, origami can be also beneficial for stretchable devices. Lin et al. reported an origami-based photodetector arrays that can withstand more than $1000\\%$ of strain, as well as bending and twisting without any performance degradation (Figure 36k).632 Benefit from the folded Miura structure, the orientation of the photodetector arrays and be adjusted to maximize the light harvesting efficiency. Tao et al. reported another kind of energy harvester (triboelectric nanogenerator) based on origami structure, as shown in Figure 36l.633 All these electronic devices represent the unique properties that origami can bring. \n\nFor soft machines, origami engineering has also enabled a series of intelligent robots. However, current strategies for either kirigami or origami are restricted by permanent deformation, requirement of prepatterning, and cannot change their shape once designed. To address these limitations, Meeussen et al. proposed a multistable shape-morphing strategies that allows the erase of shapes and structures, as shown in Figure $36\\mathrm{m}.^{634}$ This strategy does not only make reprogrammable and robust actuation possible, but also can be applied to other undulation patterns and dimensions from miniature to architectural scales. In Figure ${36}\\mathrm{n},$ , Yan and coworkers reported an autonomous origami-based robot by integrating sensing, computation, and actuation into a comprehensive structure.635 Specifically, this autonomy in soft robots were achieved by combining flexible bistable mechanisms, acting as the information processing unit, with conductive thermal materials, acting as the actuation unit. Origami robot fabricated based on this functional integration can successfully capture preys and avoid obstacles, shedding light on the future development of fully soft intelligent machines. It should be noted that origami module can also work along with other actuation modules for soft robots with more diverse movement patterns and functionalities. Drawn inspiration from octopus arms, Wu et al. demonstrated an origami-based robotic arm with multimodal deformation capabilities, such as stretching, folding, bending, and twisting, which is controlled by magnetic module, as illustrated in Figure 36o.636 Starting from investigation the magnetic actuation patterns of deploying, folding, and bending of single unit Kresling, units of this patterns exhibit sophisticated movement with higher degree of freedom, mimicking the essential functions of octopus arms. Metamorphosis is another interesting topic for soft robot. By utilizing self-folding origami exoskeletons as the metamorphosis base of robot, Miyashita et al. introduced an approach for soft robot that can change their capabilities according to needs (Figure 36p).637 The origami metamorphosis can be activated by magnetic, heat, and water. Each stimulus controls a specific movement of the robot, which is promising for soft machines working in changing environment. \n\n \nFigure 37. Fibers and fabrics for soft sensing and actuation materials. (a) Ultrasensitive textile pressure sensor. Reproduced with permission from ref 653. Copyright 2015 Wiley. (b) Fabric-based optical communications by diode fibers. Reproduced with permission from ref 654. Copyright 2018 Springer Nature. (c) Optoelectronic devices by semiconductor fiber. Reproduced with permission from ref 655. Copyright 2024 Springer Nature. (d) Chipless textile electronics by body-coupled fiber. Reproduced with permission from ref 656. Copyright 2024 Springer Nature. (e) Large-area display textiles. Reproduced with permission from ref 657. Copyright 2021 Springer Nature. (f) Rechargeable solid-state zinc-ion fiber battery. Reproduced with permission from ref 658. Copyright 2021 American Association for the Advancement of Science. (g) Metamaterial textiles for wireless body sensor networks. Reproduced with permission from ref 659. Copyright 2019 Springer Nature. (h) Tactile textiles for human−environment interaction. Reproduced with permission from ref 660. Copyright 2021 Springer Nature. (i) Electric actuated CNT artificial muscles. Reproduced with permission from ref 661. Copyright 2014 American Chemical Society. (j) Fiber pumps for wearable fluidic systems. Reproduced with permission from ref 662. Copyright 2023 American Association for the Advancement of Science. (k) Photoresponsive molecular motors. Reproduced with permission from ref 663. Copyright 2018 Springer Nature. (l) Thermal-driven fiber muscles. Reproduced with permission from ref 664. Copyright 2014 American Association for the Advancement of Science. (m) Helical fiber actuators driven by solvents and vapors. Reproduced with permission from ref 665. Copyright 2015 Springer Nature. (n) Magnetic-driven continuum fiber robots. Reproduced with permission from ref 666. Copyright 2019 American Association for the Advancement of Science. (o) Pneumatic soft robot by fabrics. Reproduced with permission from ref 667. Copyright 2023 Wiley. (p) Encoded soft textile robots. Reproduced with permission from ref 668. Copyright 2024 American Association for the Advancement of Science. \n\n \nFigure 38. Other structures for soft electronics and soft robots. (a) Mechanical integrated circuit materials. Reproduced with permission from ref 669. Copyright 2022 Springer Nature. (b) Mechanical metamaterial with stable memory. Reproduced with permission from ref 670. Copyright 2021 Springer Nature. (c) Meta-mechanotronic materials for self-powered computation. Reproduced with permission from ref 671. Copyright 2023 Elsevier. (d) Architected materials with neural learning abilities. Reproduced with permission from ref 672. Copyright 2022 American Association for the Advancement of Science. (e) Physically intelligent materials for on-board control. Reproduced with permission from ref 673. Copyright 2023 American Association for the Advancement of Science. (f) Mechanical metamaterials for counting and sequential information processing. Reproduced with permission from ref 674. Copyright 2023 American Physical Society. (g) Elastically instable materials for soft robot with high-speed and high-force. Reproduced with permission from ref 675. Copyright 2020 American Association for the Advancement of Science. (h) Dome-patterned metamaterial sheets for soft gripper. Reproduced with permission from ref 676. Copyright 2020 Wiley. (i) Buckling elastomeric materials for the actuation of soft machines. Reproduced with permission from ref 677. Copyright 2015 Wiley. (j) Self-folding materials for robots. Reproduced with permission from ref 678. Copyright 2014 American Association for the Advancement of Science. (k) Self-growing robot navigation in unstructured environments. Reproduced with permission from ref 679. Copyright 2024 American Association for the Advancement of Science. (l) Pneumatic shape-morphing elastomers. Reproduced with permission from ref 680. Copyright 2019 Springer Nature.",
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"category": " Results and discussion"
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"id": 29,
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"chunk": "# 4.3. Fibers and Fabrics \n\nFibers and fabrics, stemming from nature and refined by human ingenuity, have intertwined themselves throughout the entirety of human civilization’s narrative.638−640 From the earliest threads spun by ancient cultures to the sophisticated textiles of modern times, fibers have been integral to human life, serving as the building blocks of clothing, shelter, and countless other essentials. Through innovation and craftsmanship, humans have transformed raw fibers into intricate fabrics, weaving together stories of culture, technology, and progress. This ancient artistry continues to shape our world today in an increasing pace, as fibers and fabrics not only serve for conventional applications, such as clothing, furnishing, and industry, but also are integrated with electronic components and other functions for more possibilities.174,639,641 In recent years, there has been a growing interest in developing novel smart responsive functions for fibers and fabrics to enable seamless integration of actuators, sensors, power sources, and other components.642−645 While the marriage between fibers and fabrics and electronics holds promise for a range of applications, including wearable electronics, smart clothing, perception augmentation, health monitoring, and biomedical application, the convergence of textile technology and soft robotics opens up new possibilities for creating intelligent and adaptive systems that enhance human−machine interactions and contribute to various fields, from healthcare to remote operation and beyond.646−652 The following session will discuss the latest representative advancements made in fibers and fabrics for soft electronic devices and robotic applications. \n\nA variety of sensors based on different working mechanism have been exhaustively discussed in Section 2, and a majority of these sensors are in the form of soft film. However, it should be noted that sensors should not be restricted within this form. Fiber sensors are also possible, as the fiber-based ultrasensitive pressure sensor in Figure 37a and photodetector in Figure 37b.653,654 Figure 37c shows the wearable electronic device enabled by semiconductor fiber.655 Nevertheless, rigid silicon components are usually needed in conventional fiber electronic systems for energy supply, computation, and communication, which greatly limits the development of textile electronics. To address this, Yang et al. introduced a chipless body-coupled energy interaction mechanism using a singular fiber, eliminating the necessity for additional chips or batteries on textile surfaces (Figure 37d).656 In another work, by interlacing conductive weft and luminescent warp fibers, Shi et al. created $\\mu\\mathrm{m}$ -scale electroluminescent units at the intersections of the weft and warp, and demonstrated an integrated textile system comprising a display, keyboard, and power supply can function as a communication tool (Figure 37e).657 To solve the power supply issue of electronic textiles, Xiao et al. presented a rechargeable solid-state fiber battery, which can work for more than $500\\mathrm{~h~}$ and its capacity can maintain $98\\%$ after over 1000 charging and recharging cycles (Figure 37f).658 Except these examples, fibers can also be used for metamaterials for wireless transmission and large-area tactile sensor arrays, as shown in Figure 37g,h.659,660 \n\nActuators can either be in a single fiber form or woven textile form and driven by stimuli including electric, light, thermal, solvent, magnetic, pneumatic, etc. Figure 37i shows an electricdriven artificial yarn muscle utilizing a spinnable carbon nanotube (CNT) sheet with impressive performance metrics for torsional and tensile actuation.661 Figure 37j is another electric-driven textile-based actuator integrated with a fiber pump.662 Chen et al. illustrated the macroscopic contractile motion resembling muscle behavior in a supramolecular system primarily composed of water (Figure 37k).663 This system arises from the hierarchical self-assembly of a photoresponsive amphiphilic molecular motor and exhibits significant motion amplitudes, rapid response times, precise shape control. To lower the cost of artificial muscles and improve their performances such as hysteresis, efficiency, cycle life, Haines et al. demonstrated the feasibility of converting inexpensive, high-strength polymer fibers commonly used in fishing lines and sewing threads into efficient tensile and torsional muscles through a simple twist-insertion process, as shown in Figure 37l.664 By employing a hierarchical and helical assembly process of aligned carbon nanotubes, Chen and coworkers demonstrated the creation of actuating fibers responsive to solvent and vapor stimuli (Figure 37m).665 The nanoscale gaps between individual nanotubes and micrometer-scale gaps among the primary fibers contribute to the swift response and substantial actuation stroke and the compact coil structure enables the reversibility of rotation. Figure $37{\\mathrm{n}}$ exhibits ferromagnetic soft continuum robots in a fiber form possessing omnidirectional steering and navigation capabilities driven by magnetic, which can be used for minimally invasive robotic surgery for inaccessible lesions.666 The integration of fibers and textiles with pneumatic actuators may open avenues to wearable robotics, programmable textile soft robotics and many other possibilities. Sanchez et al. investigated the influence of knit structure and yarn material properties on textile mechanics, followed by developing 3D knit soft actuators capable of extension, contraction, and bending (Figure 37o).667 It is worth noting that the properties of the textile can be customized by knit architectures and yarn materials, resulting in the on-demand manufacturing of textilebased soft robots with personalized performance. In another work, Guo et al. introduced a methodology to streamline the construction of 3D soft textile robots through 2D sewing process, as shown in Figure $37\\mathrm{p.}^{668}$ In this technique, the actuation performance of the soft robot can be programmed and guided by its textile shells, which will expedite the development and iteration of soft robots with tailored performance for safe human−robot interactions, wearable devices, and healthcare applications.",
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"category": " Results and discussion"
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},
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"id": 30,
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"chunk": "# 4.4. Other Structures \n\nBesides the aforementioned three major types of structures for electronic devices and soft robots, buckling structures, kirigami and origami, and fibers and fabrics, there are many other types of structures that exhibit unique properties for specific applications. Compared with the previous three types of structures, the last type of structures is less matured and investigated, which is worth of being explored. If employed properly, these structures will undoubtably bring the soft sensing and actuation system into a completely new level. \n\nRecent, scientists have integrated sensing and actuating functionalities into soft matter and thus such intelligent materials can respond properly to environmental stimuli. Nevertheless, information processing in such systems has been constrained by unconventional methods with limited scalability. Helou and co-workers introduced a new type of integrated circuits materials across various scales and physical environments (Figure 38a).669 These materials are able to execute sophisticated arithmetic, number comparison, and binary data decoding into visual representations. This research establishes a connection between Boolean mathematics and kinematically reconfigurable electrical circuits, facilitating all combinational logic operations in soft, conductive mechanical materials. Chen et al. reported a design framework for a tileable mechanical metamaterial with stable memory at the unit-cell level (Figure 38b).670 It employs physical binary elements analogous to digital bits, allowing for independent and reversible switching between two stable states using magnetic actuation. Each state corresponds to a distinct mechanical response, enabling reversible cycling until reprogramming. Encoding binary instructions onto the array yields varied mechanical properties, such as stiffness and strength spanning an order of magnitude. This approach promises advanced forms of mechanical metamaterials with stable memory and on-demand reprogrammability. By integrating mechanical metamaterials, digital electronics, and triboelectric nano energy harvesting technologies into a platform, Zhang et al. demonstrated the use of digital unit cells as building blocks for synthesizing mechanical configurations, performing binary/ ternary computations, and realizing digital logic gates, as shown in Figure 38c.671 The ability to autonomously learn and maintaining this learning ability among changing circumstances is universal to living species, however, challenging to materials. In Figure 38d, Lee and co-workers introduced a new category of engineered materials that mimic the learning process of artificial neural networks (ANNs) by adjusting the stiffness of their constituent beams, laying the groundwork for the development of artificial-intelligent materials capable of learning behaviors and properties.672 Autonomous sensing and control aim to circumvent the use of bulky and intricate electronic sensors, microcontrollers, and actuators, which gains increasing attention from both scientists and engineers. To achieve this goal, He et al. presented an electronics-free, onboard-controlling method for soft robots, where the composition and structure of their bodies encompass sensing, control, and actuation feedback loops, as illustrated in Figure 38e.673 In their designs, several types of materials that are responsive to external stimuli (light, heat, solvents) are used as the control module of the robot, which provides an new idea for autonomous soft robots operating in uncertain or dynamic environments. Interestingly, Kwakernaak et al. demonstrated irreversible metamaterials capable of counting mechanical stimuli and storing the outcome (Figure 38f).674 Such metamaterials sheds light on the transient memories of complex media and paves the way for advancements in smart sensing, soft robotics, and mechanical information processing. The involvement of other types of structures in soft robotic system is also common. By exploiting mechanical instability of materials, Tang et al. demonstrated a spine-inspired soft machines capable of rapid movement, as shown in Figure $38\\mathrm{g.}^{675}$ Different from conventional soft robots that prioritize stability, this universal design principle harnesses tunable snapthrough bistability to unlock the full potential of soft robots to rapidly store and release energy within milliseconds. Furthermore, arrays of bistable have also been employed in the work of Faber and co-workers.676 In their work, soft sheets with array of patterned, reconfigurable bistable domes are integrated on 3D printed soft robotic grippers (Figure 38h). \n\n \nFigure 39. Fabrication techniques of soft electronic devices and robots by templating. (a) Simplified fabrication process of soft robot by casting. Reproduced with permission from ref 690. Copyright 2013 The American Society of Mechanical Engineers. (b) Schematic illustration of injection molding. Reproduced with permission from ref 154. Copyright 2022 Wiley. (c) Microscopic robots by photolithography. Reproduced with permission from ref 691. Copyright 2022 American Association for the Advancement of Science. (d) Simplified schematic illustration of soft lithography. Reproduced with permission from ref 692. Copyright 2007 Springer Nature. (e) Soft magnetic robot by agglutinate magnetic spray coating. Reproduced with permission from ref 693. Copyright 2020 American Association for the Advancement of Science. (f) Part of functional layer of the insect-scale soft robot fabricated by e-beam coating. Reproduced with permission from ref 694. Copyright 2021 American Association for the Advancement of Science. (g) Schematic illustration of working mechanism of stencil printing. Reproduced with permission from ref 695. Copyright 2017 American Chemical Society. (h) Schematic illustration of working mechanism of screen printing. Reproduced with permission from ref 696. Copyright 2017 Springer Nature. \n\nStoring and processing spatially distributed mechanical signals could be achieved through this structure. Due to its fast response time, mechanical instability can be also used for soft grippers. In Figure 38i, Yang et al. demonstrated a novel actuation mechanism based on the collapse of a set of elastomeric beams.677 To be more specific, when negative pneumatic pressure is applied, the elastic beam elements within these actuators experience a reversible, cooperative collapse, resulting in the generation of rotational motion, and finally the gripper also can open and close according to this rotational motion. The use of origami in soft robot has been discussed in the previous section. Different from conventional ones, Felton and co-workers further enrich the role of origami in intelligent soft machines by utilization of shape-memory composites as the hinges of soft robots.678 As the hinge is stimuli-responsive, the robot could autonomously transform into a fully functional and intricate machine from a flat paper, exhibiting the same folded patterns derived from computational origami, as shown in Figure 38j. This marriage between metamaterials with intelligent matter holds the potential for machines with autonomous behaviors. Another solution for autonomous navigating and exploring soft robots can lie in the self-growing. However, the capability to grow and maneuver effectively in unstructured scenarios is still in the developmental stages. In Figure 38k, Dottore et al. presented an autonomous growing robot inspired by climbing plants’ adaptive strategies.679 Mimicking the apical shoot, it senses and coordinates growth using additive manufacturing and a sensorized tip. Growth direction is guided by stimuli like gravity and light, enabling navigation and adaptation to the environment. The robot can twine around vertical supports for stress relief and anchorage, adjusting material printing for varied needs. These features offer potential for applications in exploring, monitoring, and constructing complex infrastructures autonomously. Siéfert with co-workers got inspirations from biological morphogenesis and reported a novel approach for elastomer plates to change their shapes under applied pressure (Figure 38l).680 It should be noted that precisely controlled airway networks enable arbitrary changes of three-dimensional shapes in this work.",
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"category": " Results and discussion"
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"id": 31,
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"chunk": "# 5. FABRICATION TECHNIQUES \n\nThis section will navigate into the realm of fabrication. The fabrication of tools represents a fundamental aspect of human behavior and culture, distinguishing us from other animals and serving as a hallmark of our species’ evolutionary success. Through tool fabrication, humans have transformed their relationship with the natural world and reshaped the course of history. Undoubtably, fabrication also plays an indispensable role in soft robotics.681−683 It is the foundation to the development and deployment of hardware (physical body) of soft robots and sensors and it is also crucial for the further development and application of intelligent soft machines.575,684,685 The revolution in the fabrication techniques marks the radical advancements in soft robots and the way they interact with human beings, as revolution in manufacturing could not only bring mass production to meet the increasing needs of general public, but also bring unexpected materialssotf uscotcuierteys.-6f8u6n−c6t8i8onGsefnoreramlloy espuenaekxipneg,cttehderaeppalriecatmiaonysctyepneasr osf fabrication forms for soft robot and sensors. Herein, we select the most representative paradigms to further discussion: templating, laser assisted fabrication, 3D printing, transfer printing, and assembly. These five different fabrication modes may work either independently or together for electronic devices or soft machines.",
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"category": " Materials and methods"
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"id": 32,
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"chunk": "# 5.1. Templating \n\nTemplating is often used for fabricating soft robot actuators, grippers, and sensors with well-defined shapes and geometries. It allows for precise control over the final structure and enables batch fabrication of identical components.683 The templates involved in the fabrication process can be made from various materials, such as rigid substrates or sacrificial materials that are later removed.551 While the range of materials and resolution can be wide for templating, each templating technique has its own working parameters,689 here’re the details for the prevalent templating techniques. \n\n5.1.1. Molding. Molding in soft robotics involves using molds to shape elastomeric materials into desired structures or components. Here we classify molding techniques into two categories according to their difference in fabrication procedure: casting and injection molding. Casting uses a mold with specific cavity to produce solid objects by pouring a liquid material into the cavity, as illustrated in Figure 39a.690 Before casting, a mold of the desired object is prepared either by 3D printing, machining, or other techniques from various materials such as wood, metal, or plastic. It represents the shape and features of the final soft robot component and thus its resolution limit is determined by that of molds. To prevent the casting material from sticking to the mold, a release agent is often applied to the mold surface to ensure easy removal of the cast part. Then, the elastomeric casting material, typically a two-part liquid silicone rubber is mixed and poured into the mold cavity followed by removal of air bubbles. Once the mixed elastomeric materials solidify, the mold is opened, and the cast part is removed. Casting offers several advantages for fabricating soft robot components, including the ability to produce complex shapes, customization of material properties, and scalability for mass production. Similarly, injection molding is another common manufacturing process used to produce large quantities of identical parts with high precision, as illustrated in Figure 39b.154 The elastomeric material is heated until it becomes molten and then injected into a mold cavity under high pressure. Overall, injection molding offers advantages in terms of precision, complexity, material selection, postprocessing requirements, efficiency, and scalability, making it a preferred choice for many manufacturing applications, especially in industries such as automotive, electronics, medical devices, and consumer products. \n\n5.1.2. Lithography. Lithography is essential in the fabrication of microelectronics, where it is used to define the intricate patterns of transistors, interconnects, and other components on semiconductor wafers.697−699 It is also employed in the production of MEMS devices, sensors, optical components, and photonic devices. Lithography relies on the principle of selectively transferring a pattern from a mask or template onto a substrate coated with a photosensitive material, known as a photoresist. The mask contains the desired pattern, which is usually created by electron beam lithography. There are a number of steps involved in this technique, such as coating, exposure, development, and etching. It should be noted that the resolution of lithography, or the smallest feature size that can be reliably patterned, depends on factors such as the wavelength of the light source, the numerical aperture of the optics, and the characteristics of the resist material. Advances in lithography technology have enabled the production of increasingly smaller features, pushing the limits of nanoscale fabrication. Benefitted from the advancements of photolithography, Reynolds et al. demonstrated a type of microscopic robots with integrated control and information systems by photolithography.691 As shown in Figure $39\\mathrm{c},$ , these autonomous robots, sized between 100 and $250\\ \\mu\\mathrm{m}$ , exhibit responsiveness to optical commands with speeds exceeding $10~\\mu\\mathrm{m}$ per second (the inset is a structure of the functional microrobot during the photolithography process). This work is interesting and insightful, laying the groundwork for widespread use in performing intricate tasks, adapting to surroundings, and external communication. \n\nSoft lithography is a versatile and widely used molding technique in soft robotics, in which a set of techniques were used in microfabrication to create patterns and structures on surfaces, as illustrated in Figure 39d.692 Unlike traditional lithography methods that involve hard materials like silicon, soft lithography uses elastomeric materials such as polydimethylsiloxane (PDMS) as molds or stamps.700 These stamps are typically created by casting from master templates made through conventional lithography or other methods. Once cured, the PDMS replica is peeled off the master mold, resulting in a negative impression of the mold’s features. This negative mold can then be used to create multiple copies of the soft robot component. There are a number of examples that employ soft lithography to fabricate soft robot components with precise features and complex geometries, such as pneumatic actuators, microfluidic channels, and sensor arrays.701−703 \n\n5.1.3. Coating. Coating refers to the process of applying a thin layer of material onto a substrate or template surface. This coating serves various purposes depending on the specific application. In soft robots, coating can be utilized to apply thin functional layers onto flexible components, enhancing their properties or adding functionalities in a controlled manner. According to the formation process of the coated film, coating can be classified into many categories, such as blade coating, spinning coating, spray coating, dip coating, e-beam coating, etc. Blade coating is a method for applying a uniform layer of liquid or viscous material onto a substrate. A blade spreads the material over the substrate’s surface and excess material is removed as the blade passes, ensuring an even layer.704 Spin coating utilizes centrifugal force to spread the liquid outward and then forms a uniform layer. In this process, a small volume of the liquid is dispensed onto the substrate’s center, which is then rapidly spun. Control over spin speed and duration determines film thickness .705 The aforementioned two coating approaches are mainly for applying thin films on a flat or 2D surface. Nonetheless, there may be cases where fabricating of thin film functional layer in arbitrary and 3D complex surfaces, in which blade and spin coating could not satisfy and spray and dip coating can provide more space. In spray coating, liquid coating materials are atomized into small droplets in the form of fine mist or aerosol using a spray gun or nozzle first, and then are propelled onto the substrate surface by compressed air or other means, as shown in Figure 39e.693 This technique offers several advantages, including uniform coverage over complex shapes or irregular surfaces, high throughput, and the ability to coat large areas quickly. As for dip coating, the substrate is dipped into the solution at a controlled rate, allowing the material to adhere to its surface. Upon withdrawal, excess material drips off, leaving behind a uniform coating layer. Dip coating also offers advantages in terms of simplicity, and the ability to coat complex shapes.706 For more precise control the thickness and composition of the films, e-beam coating, also known as electron beam evaporation, can be employed with a wide selection range of materials, including metals, semiconductors, and dielectrics.707 In this process, a high-energy electron beam is directed at a solid material, causing atoms or molecules to be ejected and form a vapor. This vaporized material then condenses onto the substrate surface, forming a thin film that can be precisely controlled in nanoscale. As shown in Figure 39f, Liang et al. demonstrated an agile insect-scale soft robots with trajectory control.694 This robot was driven by piezoelectric thin film and the thin layers in its body was fabricated by e-beam coating, indicating the power of this coating technique. \n\n5.1.4. Printing. In the realm of templating, we classify printing into stencil printing and screen printing. Both stencil printing and screen-printing offer advantages such as high resolution, scalability, and compatibility with a wide range of materials for fabrication of patterns, making them valuable techniques in the fabrication of electronic devices and soft robotics components. Nevertheless, they bear some differences. As illustrated in Figure ${39}\\mathrm{g},$ a thin sheet of material with a designed pattern is placed over the surface to be printed in stencil printing, and the stencil can be employed with various materials, such as paper, plastic, or metal.695 Ink is then applied over the stencil, followed by pushing the ink through the openings onto the surface below using a roller, brush, blade, or spray gun, until the desired image or pattern is printed on the substrate. In contrast, screen printing involves the transfer of ink through a mesh screen onto a substrate, such as paper, fabric, metal, or plastic. The process begins with the preparation of a screen mesh, typically made of porous fabric stretched tightly over a frame, and the desired stencil is created on the screen by blocking out areas where ink should not pass through. Once the screen is prepared, ink is applied to the top of the screen, and a squeegee is used to evenly distribute the ink across the screen’s surface, forcing it through the open areas of the screen and onto the substrate below (Figure 39h).696 This process allows for the creation of intricate patterns. Depending on the complexity of the design, multiple layers of patterns may be applied, with each pattern requiring a separate screen and printing pass. In short summary, both screen printing and stencil printing facilitate the transfer of designs onto surfaces. Screen printing stands out for its versatility and high resolution, making it ideal for printed electronics, especially multilayered, multimaterials devices. On the other hand, stencil printing provides simplicity and fast customization.",
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"category": " Results and discussion"
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"chunk": "# 5.2. Laser-Assisted Fabrication \n\nSince the debut of the laser in the 1960s, its unique properties, including coherence, monochromaticity, and high energy density, have revolutionized manufacturing.708 In fabrication, lasers serve multiple roles, including precise material removal, heat generation for welding, and surface modification for marking and engravin g.709−713 Laser beams are directed by computer-controlled systems, allowing for intricate designs and high levels of automation. The importance of laser-assisted fabrication lies in its ability to achieve exceptional precision, speed, and versatility across various materials, including metals, plastics, ceramics, and composites. In soft sensing devices and robotics, for examples, laser ablation and laser direct writing techniques allow for the creation of fine features and complex circuitry on flexible substrates, enabling the production of bendable, stretchable, and lightweight electronic devices.714−716 Laser cutting and laser micromachining enable the precise shaping of soft materials like elastomers and hydrogels, facilitating the creation of soft actuators and sensors. Moreover, laser-based additive manufacturing methods, such as selective laser sintering or stereolithography, allow for the fabrication of complex structures layer by layer, offering unprecedented design freedom and functionality.717 Herein, we summarize three major roles of laser in the fabrication of soft robots and sensing devices for the relevant scope of this review: cutting, engraving, and modification. \n\n \nFigure 40. Laser-assisted fabrication of soft electronic devices and soft robots. (a) Laser cutting enabled 3D electronics. Reproduced with permission from ref 718. Copyright 2022 American Chemical Society. (b) Multimaterial pneumatic soft actuators through laser cutting. Reproduced with permission from ref 719. Copyright 2021 Wiley. (c) Laser-engraved wearable sensor for physiological signals detection. Reproduced with permission from ref 720. Copyright 2020 Springer Nature. (d) Stretchable pumps enabled by laser engraving. Reproduced with permission from ref 721. Copyright 2019 Springer Nature. (e) Flexible temperature sensor via laser reduced graphene oxide. Reproduced with permission from ref 722. Copyright 2022 Elsevier. (f) UV laser patterning of elastomeric sheets for soft robots. Reproduced with permission from ref 723. Copyright 2021 American Association for the Advancement of Science. \n\n5.2.1. Cutting. Laser cutting utilizes a high-powered laser beam to precisely cut through materials, which can range from metals, plastics, and wood to glass, ceramics, and composites. The laser beam is focused onto the surface of the material, causing it to melt, burn, or vaporize along the desired cutting path. Laser cutting is an indispensable process in the manufacturing of electronic devices, serving crucial roles across multiple stages of their production. As shown in Figure 40a, it is employed in the fabrication of Printed Circuit Boards (PCBs),718 where it precisely cuts substrates and drills microvias, as well as in the dicing and singulating of semiconductor wafers and components. In the realm of flexible electronics, lasers shape substrates and also create conductive traces, enabling the production of flexible devices and wearable sensors. For soft robotics, laser cutting is employed to create intricate patterns and structures in materials such as silicone, rubber, and hydrogels, enabling the production of soft actuators and grippers, as demonstrated in Figure 40b.719 Attribute from laser cutting, 2D patterns with arbitrary shapes and sizes can be fabricated rapidly, these patterns could not only immediately serves as the working substrates or functional layers of the devices, but also can act as the enabling media for kirigami, origami, and other structures. \n\n5.2.2. Engraving. While laser cutting is primarily used for cutting through materials to create shapes and parts, laser engraving, on the other hand, involves using a laser beam to remove material from the surface of an object to create a shallow depression or etched design. In this process, instead of cutting all the way through the material, the laser beam removes only a thin layer from the surface, leaving behind a permanent mark or design. For electronic devices, laser has been widely used as an efficient tool to convert organic materials to porous graphene structures. It is worth noting that the underlying mechanism for such conversion is the carbonization and graphitization of the engraved materials by laser, whose parameter should be carefully selected for products with a specific performance. Over the years, laserinduced graphene was successfully prepared from a wide range of materials, such as woods, paper, commercial polyimide, etc.710 For example, Lin et al. presented a facile and scalable method for producing porous graphene by using a $\\mathrm{CO}_{2}$ infrared laser on commercially available polymer films.724 The laser irradiation converts $\\mathsf{s p}^{3}$ -carbon atoms to $\\mathsf{s p}^{2}$ -carbon atoms, resulting in laser-induced graphene (LIG) with high electrical conductivity. Moreover, the graphene can also be patterned into interdigitated electrodes for energy storage device applications. Apart from fabrication of single type of electronic elements, laser engraving technology can also be used to manufacture multimodal wearable devices. In another example, Yang and co-workers reported a fully laser-engraved sensor capable of simultaneously sampling sweat, sensing chemicals, and monitoring vital signs, as shown in Figure 40c.720 This multimodal device enables continuous detection of temperature, respiration rate, and low levels of uric acid and tyrosine, which indicates the possibility of such technology in scalable manufacturing of multimodal functional devices for real world applications. Besides, laser engraving technology can also be used for soft actuation or robotic system. As shown in Figure 40d, Cacucciolo et al. demonstrated a stretchable pump for soft machines.721 This stretchable pump was composed several layers of soft materials, and the electrodes in both top and bottom layers are fabricated by laser engraving. \n\n5.2.3. Surface Modification. Laser modification encompasses a variety of techniques used to alter material properties \n\n \nFigure 41. Fabrication techniques of soft electronic devices and robots based on 3D printing. Schematic illustration the working principles of commonly used 3D printing technologies, including (a) IJP, (b) DIW, (c) FDM, (d) DLP, (e) SLA, (f) TPP. (g) Printing of three-dimensional stretchable electronics. Reproduced with permission from ref 743. Copyright 2023 Springer Nature. (h) 3D printing of tissue adhesive. Reproduced with permission from ref 744. Copyright 2024 Springer Nature. (i) 3D printing of soft hydrogel electronics. Reproduced with permission from ref 745. Copyright 2022 Springer Nature. (j) 3D printing of silica aerogels. Reproduced with permission from ref 746. Copyright 2020 Springer Nature. (k) Multimaterial printing of filaments with subvoxel control enabled by nozzle rotation. Reproduced with permission from ref 747. Copyright 2023 Springer Nature. (l) 3D-printed entirely soft, autonomous robots. Reproduced with permission from ref 110. Copyright 2016 Springer Nature. (m) 3D-printed soft robot powered by combustion. Reproduced with permission from ref 748. Copyright 2015 American Association for the Advancement of Science. (n) 3D-printed biomimetic artificial muscles. Reproduced with permission from ref 749. Copyright 2022 American Association for the Advancement of Science. (o) 3D-printed Untethered soft robotic matter with passive control of shape morphing and propulsion. Reproduced with permission from ref 750. Copyright 2019 American Association for the Advancement of Science. (p) 3D-printed omnidirectional and exteroceptive soft actuators. Reproduced with permission from ref 751. Copyright 2022 American Association for the Advancement of Science. (q) Embedded 3D printing for soft somatosensitive actuators. Reproduced with permission from ref 752. Copyright 2018 \n\nWiley. (r) Proprioceptive three-dimensional architected robotic metamaterials by printing. Reproduced with permission from ref 753. Copyright 2022 American Association for the Advancement of Science. (s) Flexible electroluminescent soft robots by 3D printing. Reproduced with permission from ref 754. Copyright 2022 Springer Nature. (t) 3D-printed sensorized soft actuators. Reproduced with permission from ref 755. Copyright 2022 American Association for the Advancement of Science. (u) Soft robotic devices by vision-controlled jetting. Reproduced with permission from ref 756. Copyright 2023 Springer Nature. \n\nthrough the application of laser energy. This can include surface modification processes like laser annealing, alloying, and texturing, as well as methods for introducing dopants or additive manufacturing techniques such as selective laser melting.725,726 These processes can be used to improve the surface hardness, wear resistance, or corrosion resistance of materials, as well as to modify their electrical, optical, or mechanical properties. Laser modification finds applications in diverse fields such as surface engineering, microfabrication, additive manufacturing, and semiconductor device processing, offering versatile solutions for tailored material properties and advanced manufacturing processes, like the achievements made by other modification techniques.727,728 In a work, Chen and co-workers reported a strategy to fabricate a rapid-response flexible temperature sensor with proximity sensing capability using laser-reduced graphene oxide (Figure 40e).722 In their experiments, UV laser was utilized to reduce GO for the sensor and it was found that the combined effects of GO concentration and laser scan line spacing can influence sensor sensitivity. Zhang et al. introduced a method for programmable and reprocessable multifunctional soft robots, enabled by designing and incorporating features and functions into elastomeric surfaces, as illustrated in Figure 40f.723 Specifically, the surfaces of elastomeric sheet can be modified by selective laser scanning. Upon modification, the elastomers’ functionalities can be controlled by infusing with particle solution and solvent retreatment. These processes find applications in smart soft robot and healthcare, offering tailored material properties and advanced actuation capabilities. \n\nIn addition to laser cutting, engraving, and modification, various other laser-based fabrication techniques also play pivotal roles across industries, such as welding, sintering, marking, and so on. Together, these techniques expand the capabilities of laser-based fabrication, catering to diverse manufacturing needs across electronics manufacturing, soft robotics and beyond. Looking ahead, the prospects for laserbased fabrication are promising, with ongoing research pushing the boundaries of what is possible in terms of device functionalities and manufacturing capabilities. However, it is essential to acknowledge the limitations, such as complexity, scalability, cost, energy consumption, and material compatibility, which may pose challenges in certain applications. Nevertheless, with continued innovation and advancements in laser technology, the future holds exciting possibilities for the integration of laser-based fabrication techniques into diverse fields, driving forward the development of next-generation devices and technologies.",
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"category": " Results and discussion"
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"chunk": "# 5.3. 3D Printing \n\n3D printing, also known as additive manufacturing, is a process of creating three-dimensional objects by adding material layer by layer according to a digital design. Unlike traditional subtractive manufacturing methods, where material is removed from a solid block, 3D printing builds sophisticated structures and objects from the ground up, offering greater design \n\n5.3.1. Principles of 3D Printing. According to the working principles, 3D printing technology can be classified as inkjet printing (IJP), direct ink writing (DIW), fused deposition modeling (FDM), digital light processing (DLP), stereolithography (SLA), two-photon polymerization (TPP), selective laser sintering (SLS), etc., and Figure 41a−f illustrates the working principles and process of six types of 3D printing technologies that are widely used. \n\nInkjet printing (IJP) is a digital printing technology that utilizes small droplets of ink to create designed patterns on various substrates.737 The process involves ejecting ink droplets from a printhead (nozzle) onto the substrate in a controlled manner, driven by thermal or piezoelectric approach. In thermal inkjet printing, a tiny resistor heats up and vaporizes a small volume of ink, causing it to form a droplet that is ejected onto the substrate. In piezoelectric inkjet printing, electrically charged piezoelectric crystals change shape when exposed to an electric field, causing pressure changes that eject ink droplets from the printhead onto the substrate. Unlike traditional 3D printing methods, which rely on solid filaments or powders, direct ink writing (DIW) enables the deposition of soft, viscoelastic materials such as hydrogels, polymers, or colloidal suspensions by extruding ink or paste-like materials through a nozzle or syringe onto a substrate in a layer-by-layer fashion.738 In fused deposition modeling (FDM), thermoplastic filament is melted and extruded through a heated nozzle, which moves along a predetermined path.739 As the extruded material is deposited onto a build platform, it quickly solidifies, forming a solid layer. The build platform then moves down incrementally, and the process is repeated layer by layer until the entire object is complete. Digital light processing (DLP) uses a digital light projector to selectively cure liquid resin into a solid object.740 In DLP printers, a light source, typically a high-intensity UV lamp or LED, projects an image of each layer of the object onto a vat of liquid photopolymer resin. The image is projected onto the resin surface through a digital micromirror device, which contains millions of tiny mirrors that can tilt to either reflect light toward the resin or away from it. By selectively activating the mirrors according to the digital design, the desired pattern of light is projected onto the resin, causing it to polymerize and solidify where the light strikes. After each layer is cured, the build platform moves incrementally, and the process is repeated layer by layer until the entire object is formed. In stereolithography (SLA) printing, a liquid photopolymer resin is selectively cured, or solidified, layer by layer using a UV laser.741 The process begins with a vat of liquid resin, and a UV laser is directed onto the surface of the resin, tracing the shape of the object to be printed based on a digital 3D model. When the UV laser hits the liquid resin, it causes a chemical reaction that polymerizes the resin, transforming it from a liquid to a solid. Once a layer is cured, the build platform moves down slightly, and the process is repeated to create the next layer. Two-photon polymerization (TPP) is an advanced additive manufacturing technique used to create high-resolution three-dimensional structures at the micro- and nanoscale.742 Unlike traditional 3D printing methods that solidify materials layer by layer, TPP utilizes a focused laser beam to induce polymerization within a photosensitive resin. The process involves focusing ultrashort laser pulses (typically in the femtosecond range) at a specific point within the resin volume. When two photons of light are absorbed simultaneously by a photosensitive molecule, they combine their energy to induce a chemical reaction, causing the resin to solidify only at the focal point of the laser beam. By precisely controlling the movement of the laser beam relative to the resin, complex three-dimensional structures with submicron resolution can be fabricated. \n\n5.3.2. Printing Soft Electronic Devices. 3D printing of soft electronic devices involves the fabrication of flexible and stretchable electronic components using additive manufacturing techniques.757−760 This process enables the integration of electronic functionalities into soft and deformable materials, such as elastomers or hydrogels, to create devices that can conform to complex shapes, bend, stretch, and even withstand deformation.761 The merits of 3D printing of soft electronic devices lie in its customization, complex geometries, material flexibility, integration of functionalities, etc. Recently, there has been a vast number of works on this field. \n\nAlthough 3D printing has the potential to fabricate intricate and versatile soft electronic devices, printing solid-state elastic conductors with complex three-dimensional shapes poses a challenge due to the rheological properties of conventional inks, which typically allow only for layer-by-layer deposition. To address this issue, Lee et al. demonstrated the printing of elastic conductors in all directions enabled by an emulsion system, which is composed of a conductive elastomer composite, immiscible solvent, and emulsifying solvent (Figure 41g).743 By this approach, the direct writing of freestanding, filamentary, and out-of-plane three-dimensional shapes is possible with a minimum feature size of less than $100\\ \\mu\\mathrm{{m}}$ and a stretchability of over $150\\%$ . In another work, Wu and coworkers introduced a novel 3D printable tissue adhesive produced via direct-ink-writing technology, as shown in Figure 41h.744 This adhesive allows for the fabrication of flexible, stretchable, biocompatible bioadhesive patches and devices with customizable architectures, opening up new avenues for tailored designs to suit specific applications. Electronics fabricated from hydrogels bear intrinsic resemblances to biological tissue and hold significant promise for biomedical applications, and such devices should feature customizable three-dimensional circuits. However, the production of sophisticated three-dimensional circuits embedded within a hydrogel matrix poses challenges using current materials and manufacturing techniques. In Figure 41i, by employing a curable hydrogel-based supporting matrix and a stretchable silver-hydrogel ink, Hui et al. presented a novel approach for three-dimensional printing of hydrogel electronics.745 Owning to the yield stress fluid behavior of the matrix, it is possible for the precise placement of silver-hydrogel ink circuits and electronic components within. This approach enables the creation of electronic devices such as strain sensors, inductors, and biological electrodes, showcasing its versatility and potential for various applications. Printed electronics in extreme conditions, such as high temperature, is also of significance, with thermal insulation being one of the most predominant markets. Zhao et al. addressed the challenges in printing of silica aerogels with a direct ink writing method using a slurry of silica aerogel powder and a diluted silica nanoparticle suspension, leveraging shear-thinning behavior to enable easy flow during printing while maintaining shape postprinting, as exhibited in Figure 41j.746 The deployment of multimaterial 3D printing with precise subfeatures control could provide design space for generating multifunctional architected structures. Larson and co-workers presented a rotational multimaterial 3D printing platform that enables subvoxel control over the local orientation of azimuthally heterogeneous architected filaments (Figure 41k).747 By continuously rotating a multimaterial nozzle with a controlled ratio of angular-to-translational velocity, helical filaments with programmable helix angle, layer thickness, and interfacial area between multiple materials within a cylindrical voxel are successfully fabricated. This method further extends the capability of 3D printing technology for generating multifunctional architected matter inspired by biological motifs. \n\n5.3.3. Printing Soft Actuators. Roboticists are exploring the creation of biologically inspired robots featuring soft or partially soft bodies, which hold promise for increased durability, adaptability, and safety in human interactions compared to traditional rigid robots. However, significant hurdles in the design and production of soft robots persist, including intricate fabrication processes and the integration of soft and rigid elements. 3D printing technology offers unique advantages in the production of soft actuators, allowing for the precise control of geometry, internal structure, and material composition. $732,762{\\-}{-}764\\$ This enables the creation of complex, customized actuators with tailored mechanical properties and functionalities with response to external stimuli such as heat, electricity, or light.765,766 \n\nDespite the progress made on soft robots, most of them still rely on tethering to rigid robotic control systems and power sources, which may limit their potentials in unstructured environments and other conditions. To fully unleash their potential, Wehner and co-workers introduced an entirely soft, autonomous robots using 3D printing technology, as shown in Figure 41l.110 In this work, not only the body, but the rest parts of soft robot, such as control and actuation module, are composed entirely of soft materials. For example, the control is achieved by microfluidic logic that autonomously regulates fluid flow, thus controlling the catalytic decomposition of an onboard monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation, which facilitates the programmable integration of multiple materials and functions within autonomous robots. In another work, a combustionpowered robot with a body that transitions from a rigid core to a soft exterior was fabricated using multimaterial 3D printing by Bartlett et al., as shown in Figure $41\\mathrm m$ .748 This contrasting stiffness contributes to the seamless integration between the rigid driving components and the soft body, thereby enhancing the untethered jumping performance of the robot. To replicate the versatility and elegance of movements observed in complex muscle arrangements, Pascali et al. introduced a novel class of pneumatic actuators that are designed to contract and extend according to a mathematical model (Figure 41n).749 They can be implemented at various dimensional scales and with diverse materials and mechanical capabilities, such as a pneumatic artificial hand that can be fully three-dimensionally printed in a single step, enabling the emulation of lifelike movements. \n\n \nFigure 42. Various transfer printing technologies for soft electronic devices and robots. Schematic illustration of working principles of three different mechanically guided transfer printing technologies, including (a) kinetically controlled, (b) reversible adhesion, and (c) shear-enhanced. Reproduced with permission from ref 156. Copyright 2006 Springer Nature. Reproduced with permission from ref 776. Copyright 2010 United States National Academy of Sciences. Reproduced with permission from ref 777. Copyright 2011 American Institute of Physics. Schematic illustration of working principles of three different stimuli-triggered transfer printing technologies, including (d) laser-driven, (e) thermal controlled, and (f) magnetic controlled. Reproduced with permission from ref 778. Copyright 2023 American Association for the Advancement of Science. Reproduced with permission from ref 779. Copyright 2021 Wiley. Reproduced with permission from ref 780. Copyright 2019 Elsevier. Schematic illustration of other working principles for transfer printing, including (g) hydroprinting, (h) UV tape-assisted, (i) capillary forceassisted. Reproduced with permission from ref 781. Copyright 2017 Wiley. Reproduced with permission from ref 782. Copyright 2024 Springer Nature. Reproduced with permission from ref 783. Copyright 2017 American Chemical Society. (j) Large-area, conformable tattoo-like electrodes on human body. Reproduced with permission from ref 784. Copyright 2020 American Association for the Advancement of Science. (k) Wrap-like transfer printing for three-dimensional curvy electronics. Reproduced with permission from ref 785. Copyright 2023American Association for the Advancement of Science. (l) Conformable flexible sheets on spherical surfaces by transfer printing. Reproduced with permission from ref 786. Copyright 2023American Association for the Advancement of Science. \n\nBesides the above 3D printing examples for soft actuators, this technology can also be used for printing of intelligent soft materials for robots that are capable of undergoing repeated shape-morphing and self-propulsion in reaction to external cues. In Figure 41o, Kotikian and co-workers utilized 3D printing to produce soft robotic materials comprising bilayers of liquid-crystal elastomers with orthogonal director alignment and varying nematic-to-isotropic transition temperatures.750 By modifying their chemistry and printed structure, the actuation behavior of the robot can be tailored, capable of assuming taskspecific configurations as needed. As economy and technology advance, incorporating sustainability principles into the development of novel fabrication methods is imperative. For this, Heiden et al. introduced a 3D printing process to fabricate fully biodegradable pneumatic actuators demonstrate omnidirectional movement with fast response times (Figure 41p).751 Additionally, they can be reprinted multiple times or disposed of without hazard at the end of their lifespan, potentially paving the way for a sustainable future in soft robotics. \n\n5.3.4. Printing Soft Robots with Sensing Abilities. Moving further, sensing is essential for soft robotics as it enables these flexible and versatile systems to perceive, understand, and interact with their surroundings, and 3D printing of soft robots with sensing abilities is undoubtably one of the most promising ways to unlock the potential applications of this. In Figure 41q, Truby et al. employed embedded 3D printing technology to fabricate soft somatosensitive actuators.752 Multiple conductive features incorporated into a soft robotic gripper can provide proprioceptive and haptic feedback through embedded curvature, inflation, and contact sensors. This work facilitates the integration of complex sensing mechanisms into soft actuating systems, representing a crucial step toward achieving closed-loop feedback control in soft robots, machines, and haptic devices. In another work, Cui et al. introduced a design and manufacturing approach to produce a novel category of proprioception robotic metamaterials with self-sensing and feedback control, which could not only have multimodal locomotion abilities, but also can actively sense and execute movements (Figure 41r).753 \n\nPrinting of soft robot with sensing abilities can also be achieved by other principles. For example, 3D printable inks with ion-conducting, electroluminescent, and insulating dielectric properties are developed by Zhang et al., and this ink enables effortless and customizable 3D printing of flexible electroluminescent devices and soft robotics (Figure 41s).754 \n\nBy integrating the printed electroluminescent devices with a soft quadrupedal robot and sensing units, an artificial camouflage that can adapt to the environment by displaying matching colors is demonstrated, establishing an effective framework for next-generation soft camouflages. In another work, Truby et al. proposed a novel approach to sensorize architected materials via fluidic innervation.755 As illustrated in Figure 41t, networks of empty, air-filled channels are directly embedded within the actuator, the deformation information on the materials can be obtained by monitoring pressure changes within these channels. By 3D printing of sensorized structures from a single material, this strategy streamlines the design of sensorized materials by integrating structural, sensing, and actuation capabilities solely through geometric control, with applications ranging from wearables and smart structures to robotics. Although 3D printing examples of soft sensing robot keep increasing, the task of automatically and rapidly fabricating functional systems with diverse elastic properties, resolutions, and integrated actuation and sensing channels remains a significant hurdle. Buchner et al. propose a novel inkjet deposition process enables the creation of complex systems and robots, as shown in Figure 41u.756 The printing geometry was captured in this process to eliminate the need for mechanical planarizers, enabling the printing of a wider range of materials and elastic moduli. This approach offers an automated, scalable, and high-throughput process for producing sophisticated structures, functional devices, and robotic systems with enhanced performances.",
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"chunk": "# 5.4. Transfer Printing \n\nThe increasing popularity of flexible and soft functional devices has driven the integration of components and functions on a platform seamlessly. Such components with high performance are usually fabricated on conventional substrates such as silicon wafer and should be transferred to the target substrate for the integration of flexible functional device. Transfer printing technology serves as a crucial technique for integrating diverse cdoevmicpeos antds sinoftt sopbaottisa.l7l6y o7r7g3 nIti edn, fumnpcatsisoens erleacntgre iocf methods and techniques to reorganize materials, patterns, or functional components from one substrate to another. Such methodological approaches provide a versatile platform technology and adaptable means of fabricating high-performance, heterogeneously integrated functional systems at a costeffective scale.711,774,775 The key to the successful transfer printing is the proper control of energy release across different interfaces and there are several types of controlling strategies, such as mechanically guided, stimuli-triggered, and others. \n\n5.4.1. Mechanically Guided. Mechanically guided transfer printing leverages the adhesion strength between the donor and receiver interface to achieve controllable adhesion and release of the components by regulate the interfacial mechanics. Meitl et al. reported a universal technique for transferring of different components based on a kinetically controlled approach by using an elastomeric stamp (Figure \n\n42a).156 Specifically, they found the adhesion strength is dependent on the separation speed between two object and they can successfully control the transfer process by properly control the speed. This technique is not only facile and easy, but also applicable to different types of materials and sizes and shapes. In Figure 42b, Kim et al. provided another solution via pressure induced switching of adhesion strength between rigid objects and elastomeric surfaces to deterministically transfer of components.776 This adhesion strength can be reversibly switched by over 3 orders of magnitude. Besides, Carlson and co-workers introduced a shear-enhanced transfer printing technology for deterministic materials assembly, as illustrated in Figure 42c.777 They found the adhesion behavior could be effectively regulated by directional shear strain. Through analytical and finite element modeling, along with practical printing demonstrations, they uncovered the fundamental mechanics behind this process and showcase its potential for material assembly. \n\n5.4.2. Stimuli-Triggered. Another commonly used transfer printing technique is stimuli-triggered transfer printing. In this technique, the adhesion strength between the transferred objects and the stamps can be controlled by external stimuli, such as laser, heat, vacuum, etc. By carefully engineering the properties of the materials involved, such as their adhesion strength, responsiveness to stimuli, and surface characteristics, transfer printing can be initiated or halted on demand. Chen and co-workers introduced a laser projection proximity transfer technique, in which the distance between the chip and stamp and their contact area can be regulated by hierarchical gasneedles stamp, as shown in Figure 42d.778 Combining exceptional adhesion switchability $_{\\sim1000}$ times) and high transfer accuracy $(\\sim4\\ \\mu\\mathrm{m})$ , this technique exhibits remarkable capabilities for deterministic microarray assembly, exemplified by its application in programmable microtransfer printing of MicroLED microchips for flexible displays. In addition, Luo et al. introduced a thermally controlled tunable adhesive capable of both eliminating interfacial adhesion during printing and enhancing it for pick-up, enabled by thermal-controlled suction and thrust, which not only facilitates reliable and damage-free transfer printing, but offer insights into the design and operation of the thermally controlled tunable adhesive (Figure 42e).779 In another work, Linghu et al. developed a magnetcontrolled transfer printing method and also constructed an analytical mechanics model to further elucidate the corresponding underlying mechanism, as illustrated in Figure 42f.780 \n\n5.4.3. Other Transfer Printings Methods. Apart from the previously mentioned mechanically guided and stimulitriggered transfer printing technologies, a range of alternative approaches are being employed for material transfer. These include hydroprinting, UV tape-assisted transfer printing, and capillary force-assisted transfer printing Figure $\\hat{4}2g\\mathrm{-i}$ 781−783 Each of these techniques is tailored for specific transfer scenarios. For instance, hydroprinting offers distinct advantages for transferring patterns onto 3D structures or curved surfaces, while UV tape-assisted transfer printing and capillary force-assisted transfer printing excel in transferring 2D materials with high fidelity, thereby expanding the possibilities for diverse performance and functionality. \n\n5.4.4. 3D Curvy Electronics via Transfer Printing. The prevalence of 3D curved surfaces in both natural and industrial environments has catalyzed the emergence of 3D curved electronics, which can unleash many possibilities including conformable bioelectronics, antennas, bioinspired electronic eyes, metasurface engineering, and soft robotics.787−790 However, different from planar electronics, design and fabrication soft electronic devices that can be conformable to any 3D surface is challenging to both scientists and engineers. Transfer printing could be one of the most promising and effective way to fabricate electronics in a 3D manner. 791 For examples, Wang et al. presented a transfer process inspired by Cartan curves to creating large-area, soft, breathable, and conformable electrodes that can cover extensive areas such as the entire chest, forearm, or neck of human beings (Figure 42j).784 Chen et al. introduced an automated wrap-like transfer printing prototype, which is suitable for fabricating 3D curvy electronics. In this method, prefabricated planar circuits are seamlessly integrated onto the target surface, ensuring complete coverage, with the aid of a petal-like stamp under a gentle and uniform pressure field (Figure 42k).785 In Figure 42l, Liu and co-workers employed a blend of experimental, analytical, and numerical methodologies to thoroughly examine the conformability of circular sheets on spherical surfaces.786 By scrutinizing the buckling behavior of thin films on curved surfaces, a scaling law that accurately forecasts the conformability of flexible sheets on spheres is proposed in their work, providing insights for the future development of conformable electronics. \n\n \nFigure 43. Fabrication by assembly of components at different level scales: $(\\mathsf{a}\\mathrm{-}\\mathsf{d})$ materials level, $\\left(\\mathrm{e-h}\\right)$ electronic device level, (i−l) robotic system level. (a) Plasmonic welding of silver nanowire junctions. Reproduced with permission from ref 796. Copyright 2012 Springer Nature. (b) Direct bonding of gold electrodes onto ultrathin polymer films. Reproduced with permission from ref 797. Copyright 2021 American Association for the Advancement of Science. (c) Autonomous alignment and healing in multilayer polymers. Reproduced with permission from ref 798. Copyright 2023 American Association for the Advancement of Science. (d) Biphasic, nanodispersed interface for connection and encapsulation of soft-rigid electronic devices. Reproduced with permission from ref 799. Copyright 2023 Springer Nature. (e) Assembly of 3D electronics. Reproduced with permission from ref 800. Copyright 2018 Springer Nature. (f) Deterministically assembly of 3D curved mesosurfaces using microlattice designs. Reproduced with permission from ref 801. Copyright 2023 American Association for the Advancement of Science. (g) Highdensity bimodal sensor arrays by assembly. Reproduced with permission from ref 802. Copyright 2022 Wiley. (h) Monolithically integrated, lowvoltage, soft e-skin with capabilities of multimodal perception, neuromorphic pulse-train signal generation, and closed-loop actuation. Reproduced with permission from ref 803. Copyright 2023 American Association for the Advancement of Science. (i) Assembly of submillimeter-scale multimaterial terrestrial robots. Reproduced with permission from ref 804. Copyright 2022 American Association for the Advancement of Science. (j) Robotic system that can change its physical shape and compliance by collective assembly. Reproduced with permission from ref 805. Copyright 2024 American Association for the Advancement of Science. (k) Photograph of a robotic structural system that assembles individual block into a whole programmable structure. Reproduced with permission from ref 806. Copyright 2024 American Association for the Advancement of Science. (l) 3D miniature magnetic soft machines via multimaterial heterogeneous assembly. Reproduced with permission from ref 807. Copyright 2021 American Association for the Advancement of Science.",
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"chunk": "# 5.5. Assembly \n\nAssembly typically involves the process of combining multiple parts or layers to create a new structure or device with innovative functionalities and form factors.792 This process often entails combining materials like polymers, metals, and semiconductors to form flexible substrates, circuits, sensors, and displays.595,789,793,794 For instance, in flexible sensors or wearable electronics, assembly involves integrating sensing elements, electronic components, and flexible substrates into a single device that can conform to the contours of the human body or other flexible surfaces.795 Here, according to the degree and complexity level of integration, we divide assembly into three categories: assembly on materials level, electronic device level, and robotic system level. \n\n5.5.1. Materials Level. Starting from materials, assembly in this level involves the integration of two or more materials to form a single functional part, utilizing techniques such as welding, bonding, self-healing, and other principles. Garnett et al. presented a novel method called light-induced plasmonic nanowelding, which facilitates the assembly of metallic nanowires into large interconnected networks (Figure 43a).796 In this technique, light concentrate on the small gaps at nanowire junctions that need to be joined, ensuring physical connections between wires without causing damage to substrates. Takakuwa and co-workers introduced water vapor plasma-assisted bonding approach to facilitate the direct bonding of gold electrodes onto ultrathin polymer films in room temperature and atmospheric pressure (Figure 43b).797 In this process, the plasma can generate hydroxyl groups that aid in the bonding process between two gold surfaces, resulting in the formation of a robust and enduring interface. Cooper et al. also employed healing multilayered and functional polymers to assembly two materials together.798 As shown in Figure 43c, two distinct dynamic polymers with incompatible backbones yet sharing identical dynamic bonds can autonomously realign during this healing process. Jiang et al. created a biphasic, nanodispersed interface capable of seamlessly connecting soft, rigid, and encapsulation modules for durable and highly flexible devices (Figure 43d).799 This plug-and-play interface may streamline and expedite the development of stretchable devices for on-skin and other applications. \n\n5.5.2. Electronic Device Level. In contrast to materiallevel assembly, electronic device-level assembly emphasizes the comprehensive functionalities of flexible electronic devices by integrating diverse materials, functional components, and other electronic devices to create a platform ready for immediate use. For example, Huang et al. presented a framework for the development of 3D integrated stretchable electronics with higher integration density on stretchable substrates and new functionalities compared with their conventional counterparts (Figure 43e).800 This approach involves constructing threedimensional devices layer by layer, employing transfer printing to place predesigned stretchable circuits onto elastomers and creating vertical interconnect accesses using laser ablation and controlled soldering, which is an exemplar implementation of assembly at electronic device level. Cheng et al. devised a mechanically guided assembly of programmable 3D curved mesosurfaces from 2D films, with demonstration for a conformable electronic device for cardiac and cell scaffold (Figure 43f).801 In another work, Cui et al. introduced an assembly approach for integrating 112 bimodal sensors into a thin, conformal, and stretchable tactile glove for body features extraction with an accuracy of $98\\%$ enabling the digitalization of tactile information, as shown in Figure $43\\mathrm{g.}^{802}$ By materials and structure design, Wang et al. demonstrated an e-skin that incorporates organic semiconductor transistors with capabilities including multimodal perception, neuromorphic pulsetrain signal generation, and closed-loop actuation (Figure \n\n43h).803 This assembled electronic device bears merits like low subthreshold swing, low operation voltage, low power consumption, and moderate-scale circuit integration complexity. \n\n5.5.3. Robotic System Level. At the robotic system level, assembly entails a more complex integration process compared to material or electronic device levels. This is because it involves not only materials properties, functions, and electronic devices, but also actuators, sensorimotor controllers, communication elements, and various other components. Moreover, several factors outside of aforementioned points must be carefully considered at this level, including systematic reliability, compatibility, robustness, etc. Han and co-workers demonstrated terrestrial robots with intricate, 3D geometries and multimodal movement capabilities enabled by heterogeneous material assembly (Figure 43i).804 Looking from another perspective, assembly should not be restricted from individual components or elements, and one robotic system could also assemble with another system to form a larger collective system. In response, Saintyves et al. reported a robotic system capable of altering both their physical shape and compliance to accommodate environmental constraints (Figure 43j).805 This system comprises gear-like units, each housing a single actuator, enabling units to self-assemble into larger granular aggregates. Gregg et al. also devised a robotic structural system that assembles individual block into a whole programmable structure, facilitating robust collective automated assembly and reconfiguration of large functional structures using robots (Figure 43k).806 In addition, Zhang et al. proposed a bottom-up assembly-based approach to 3D microfabrication of wireless magnetic soft machines at both the milli- and submillimeter scales (Figure 43l).807 This assembly method allows for the fabrication of structures with arbitrary multimaterial compositions, 3D geometries, and programmable magnetization profiles biomedical engineering applications.",
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"category": " Results and discussion"
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"chunk": "# 6. SENSORIMOTOR CONTROL \n\nSensorimotor control involves the coordination of sensory perception (as discussed in Section 2) with motor commands, or actuation (as discussed in Section 3) to enable a robot to perceive its environment and act accordingly. It is the process by which a robot uses its sensors (such as vision, touch, temperature, or proximity sensors) to gather data continuously about its surroundings and then adjusts its movements or behaviors based on this information.808−810 This control paradigm allows soft robots to interact with and navigate through their environment in a dynamic and adaptive manner. For example, a robot equipped with sensorimotor control can avoid obstacles, respond to stimuli in real-time, or manipulate objects with precision by constantly adjusting its actions based on real-time sensory feedback. Nowadays, researchers and engineers employ various techniques, including feedback control loops, machine learning algorithms, and sensory fusion strategies, to develop sophisticated sensorimotor control systems for soft robotics. This sensorimotor control architecture forms the cornerstone of next-generation soft robotic systems, offering unprecedented levels of autonomy, adaptability, and efficiency in diverse operational contexts.811",
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"category": " Introduction"
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"chunk": "# 6.1. Sensorimotor Control Frameworks \n\nIn essence, sensorimotor control mimics the way humans and animals interact with the world around them, where sensory input guides motor actions, enabling robots to perform tasks effectively and autonomously.152,812 Figure 44a illustrates the system architecture of a sensorimotor control framework and corresponding components involved.813 In the middle part of this classical sensorimotor control architecture, mechanical system (musculoskeletal system) represents the actuator, and sensory system (sensory receptors) the sensor. When soft robots interact with the environment or perform tasks, various information are collected by the sensors and then sent to controller (central nervous system) for further processing.814 Based on this information, different actuation commands are sent to the mechanical system in real time, and soft robots can respond to stimuli correspondingly. This cycle of task− sensor−controller−actuator enable the establishment of a closed-loop feedback for exteroception. It is worth to mention the internal state of the musculoskeletal system can also be reflected by sensory receptors. This internal physical stimulation is the proprioception of such system, providing the robot with awareness of its own body position, orientation, and movement, further contributing to its ability to execute tasks accurately and efficiently. Overall, the synergistic work between proprioception and exteroception serve as the foundation for enabling soft robots to interact with their environment, perform tasks, and exhibit adaptive behavior, akin to the sensory-motor capabilities observed in biological organisms. \n\n \nFigure 44. Sensorimotor control frameworks for biological species and soft robots. (a) System architecture of a sensorimotor control framework and the main components involved in. Reproduced with permission from ref 813. Copyright 2007 American Association for the Advancement of Science. (b) Sensorimotor pathway in controlling locomotion for a cockroach, which represents a general model for sensorimotor control. Reproduced with permission from ref 815. Copyright 2000 American Association for the Advancement of Science. (c) Schematic of the neuromechanical system of locomotion in a hierarchical organization for vertebrates. Reproduced with permission from ref 816. Copyright 2023 The Company of Biologists. (d) Flow diagram of motor control principles with examples of robots. Reproduced with permission from ref 817. Copyright 2023 American Association for the Advancement of Science. \n\nTable 3. Comparison of Modal-Based and Data-Driven Control of Soft Robot \n\n\n<html><body><table><tr><td colspan=\"2\"> Approach</td><td> Principles</td><td>Advantages Assumes entire body bends with aSimplifes the mathematical modeling</td><td>Disadvantages Limited accuracy for large</td><td>Applicability Uniform bending situations where the complexity of variable curvature is not</td></tr><tr><td rowspan=\"10\">Model- based</td><td>Constant curvature (CC)</td><td>constant curvature</td><td> and control of bending segments</td><td>deformations or nonuniform curvature</td><td>necessary</td></tr><tr><td>Variable curvature (VC)</td><td>length of the segment</td><td>Allows curvature to vary along the More accurate representation of deformations, especially for complex</td><td>More complex to model and control, higher computational cost</td><td>Complex soft robotic structures where curvature varies along the length of the segment</td></tr><tr><td>Piecewise constant</td><td>Assumes segments bend with</td><td>bending Simplifies control and kinematic</td><td> May not capture complex</td><td>Continuum robots and soft robotic arms where smooth bending motions are</td></tr><tr><td>curvature (PCC) Piecewise constant</td><td>constant curvature Assumes constant strain within</td><td>modeling Simplifies computational efforts, suitable</td><td>deformations</td><td>required May notcapturehighstraingradientsStructuralanalysis,stres,and straincalculations where the deformation is linear</td></tr><tr><td>strain (PCS) Cosserat rod</td><td>each finite element Extends beam theory to include</td><td>for less complex strain variations Detailed description of deformations</td><td>accurately Complex to implement</td><td>and predictable within each segment Used in scenarios where the bending and twisting of flexible structures are critical</td></tr><tr><td>theory Hyperelastic</td><td>shear deformations Constitutive models for stress-</td><td>Captures nonlinear elastic behavior</td><td>Requires material-specific parameters</td><td>such as tentacles and manipulators in soft robotics. Modeling soft materials with nonlinear stress-strain relationships, like silicone in</td></tr><tr><td> material models</td><td>strain relationship of soft materials Mesh of finite elements for</td><td> accurately</td><td></td><td>soft robotics</td></tr><tr><td>Finite element method (FEM)</td><td>detailed deformations Based on experimental data or</td><td>Highly accurate, models complex interactions</td><td>Computationally intensive</td><td>Comprehensive simulations of stress, strain, and deformation in complex structure:</td></tr><tr><td>Black-box models</td><td>simulations Based on physical principles of</td><td>Adaptability, scalability, more computationally efficient</td><td>Lack of transparency, interpretability, and generalization</td><td>Efficient in handling complex and nonlinear relationships in various contexts.</td></tr><tr><td>White-box models</td><td>materials</td><td>Clear understanding of underlying physical principles and mechanisms</td><td>Labor-intensive and computationally expensive</td><td> Suitable for scenarios where detailed understanding of system mechanics is critical</td></tr><tr><td>Data-</td><td>Gray-box models</td><td>Based on physical principles and empirical results</td><td>Offers a blend of transparency and empirical flexibility</td><td>Requires significant computational resources and validation efforts</td><td>Versatile for scenarios where a blend of theory and data-driven insights is beneficial</td></tr><tr><td>driven</td><td></td><td> Supervised learning Based on labeled data learning</td><td>Structured learning and good interpretability</td><td>Requires large amounts of labeled data for training and limited adaptability</td><td>Can train models to classify specific motions or states based on labeled training data, enabling precise control and interaction</td></tr><tr><td></td><td>Reinforcement learning</td><td>Based on data learning</td><td>Adaptive, continuous learning, and versatility</td><td>Requires significant computational resources and time</td><td>Can enable soft robots to adapt their grasp strategy based on feedback from sensors andthesuccess ofpreviousattempts,improvinggrasping efficiencyandreliability</td></tr></table></body></html> \n\nTo make this sensing-action loop more intuitive and clearer, sensorimotor pathway in controlling the locomotion of a cockroach is provided in Figure 44b.815 The interaction between the central nervous system (CNS) and the musculoskeletal system orchestrates the motor actions of an animal. The CNS generates motor commands that prompt the musculoskeletal system to act upon the external environment. Concurrently, the external environment is sensed through various modalities, and this sensory information is relayed back to the CNS for processing. Sensory feedback can be broadly categorized into guidance and equilibrium cues from diverse modalities, represented together as light blue, and rapid phasic feedback from mechanosensors, depicted in dark blue. The CNS integrates this sensory feedback and adjusts the motor commands accordingly, facilitating adaptive responses. Simultaneously, viscoelastic mechanical preflexes, depicted in red, swiftly counteract perturbations, contributing to the animal’s stability and agility in dynamic environments. This intricate interplay between sensory feedback, motor commands, and mechanical preflexes enables animals to navigate and interact effectively with their surroundings. \n\nLooking from the point of view of neuromechanics, sensorimotor control in vertebrates is more complexed and exhibits a hierarchical organizing structure, as shown in Figure 44c.816 In such hierarchical organization, central pattern generators (CPGs) serve as the foundation, generating rhythmic commands that act as feedforward signals for coordinating basic tasks and movements. Descending commands from higher brain centers then activate and modulate these CPGs, allowing for task selection and refinement of motor patterns based on contextual demands and goals. Additionally, reflex pathways contribute by providing rapid and stabilizing responses to external perturbations, ensuring stability and adaptability during motor execution. This hierarchical framework illustrates how different levels of neural control collaborate to orchestrate complex behaviors, with CPGs providing fundamental rhythms, descending commands refining motor output, and reflexes ensuring robustness in the face of environmental challenges. \n\nAdvancements in understanding the interneuronal networks and cellular mechanisms involved in sensorimotor control have revealed the multifaceted integration of sensory feedback. It is now evident that sensory information is processed at multiple levels within the nervous system. First, sensory feedback operates through distinct reflex pathways, enabling rapid and automatic responses to immediate stimuli. Additionally, sensory signals modulate CPGs, influencing rhythmic motor patterns and coordination, such as walking or swimming. Moreover, these inputs converge with longer-latency feedback loops, integrating information to regulate higher-order functions such as navigation, task selection, and other adaptive and goal-directed behaviors. This hierarchical integration of sensory feedback underscores the complexity and sophistication of sensorimotor control mechanisms, highlighting the intricate interplay between neural circuits and sensory inputs in orchestrating motor actions and behaviors. \n\nThe principles of motor control elucidated through studies on biological systems have not only advanced our understanding of fundamental science but have also been instrumental in shaping the development of robotic systems. By studying how animals control their movements, researchers have gained valuable insights into the underlying neural mechanisms, sensory processing, and biomechanical principles involved in motor control. These insights have, in turn, inspired the design and optimization of robotic control algorithms, sensor technologies, and mechanical architectures, as depicted in Figure 44d.817 For example, rhythmic bursts of activity generated by CPGs can facilitate coordinated movements of robots, as the four-legged walking robots marked by teal. Additionally, motor circuits receive feedback from mechanosensory and proprioceptive receptors in the body and limbs, enabling reflex-based control (robots marked by red). Moreover, robot marked by red and teal employes CPG dynamics adjusted by mechanosensory feedback to enhance locomotion robustness and efficiency in uncertain environments. \n\nOverall, the translation of core motor control principles from biological systems to robotics not only advances our scientific understanding but also drives innovation in engineering, leading to the development of more capable, adaptive, and efficient robotic systems with a wide range of applications across industries.",
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"category": " Results and discussion"
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"id": 39,
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"chunk": "# 6.2. Control of Soft Robots \n\nThe control of soft robots presents unique challenges due to their compliant and deformable nature.818−821 Unlike traditional rigid robots, soft robots often lack precise and predictable kinematics, making traditional control methods less applicable. However, several approaches have been developed to address these challenges and effectively control soft robots. A comparison between the control approaches used in soft robots is made in Table 3. \n\n6.2.1. Model-Based Control. Model-based control of soft robots involves using mathematical models that accurately describe the behavior of the robot’s deformable structure and actuators.822−824 Unlike rigid-body models commonly used in traditional robotics, these models incorporate the complex mechanics of soft materials, accounting for their nonlinear elasticity, compliance, and deformation characteristics. By developing appropriate models tailored to the unique characteristics of soft materials, such as finite element method (FEM) model,825 piecewise constant strain (PCS) model,826 and piecewise constant curvature (PCC) model,827 the relationship between actuator inputs and resulting deformations can be accurately predicted and thus the full potential of soft robotics can be entirely unlocked. \n\nDrawing inspirations from humans performing interactive tasks, Jiang et al. proposed a hierarchical control system for soft arms, which is composed of three levels: a low-level controller for motion control of the arm tip, a high-level controller for behavior control based on the low-level controller’s outputs, and a top-level planner for task selection (Figure 45a).828 \n\n \nFigure 45. Control of soft robot, including model-based control, data-driven control, and hybrid control. (a) Soft manipulators by hierarchical control. Reproduced with permission from ref 828. Copyright 2021 SAGE Publications. (b) Soft manipulator for dynamic tasks. Reproduced with permission from ref 829. Copyright 2023 Wiley. (c) Octopus-inspired sensorized soft arm. Reproduced with permission from ref 137. Copyright 2023 American Association for the Advancement of Science. (d) Photograph and schematic illustrations of the soft robotic flatworm during locomotion. (e) Control framework of the soft robotic flatworm. Reproduced with permission from ref 832. Copyright 2023 Wiley. (f) Architecture of control hierarchy inspired by octopus. (g) Schematic illustration of the working process of the hierarchical control. (h) Architecture of neural network energy shaping control. Reproduced with permission from ref 835. Copyright 2023 Wiley. \n\nSpecifically, two control models are employed to achieve motion control of the soft arm during interactions with the environment: simplified Jacobian model and Q-learning-based control model. Benefit from such models, the soft arms can perform interaction tasks akin to humans, without the need for sensory inputs or additional environment models. In order to make soft robot move faster and manipulate more efficiently, Fischer et al. introduced a dynamic model by incorporating additional elements such as variable stiffness and actuation behavior in soft robotic manipulators for their dynamic control, as shown in Figure 45b.829 In another work, Xie and co-workers reported an octopus-inspired soft robotic arm integrated with sensorized skin that are capable of reaching, sensing, grasping, and interacting with the environments (Figure 45c).137 The effective movement and interaction of this soft arm is based on a bending-elongation propagation model, offering insights into the development of soft electronic devices and actuators and their integration. \n\n \nFigure 46. Other emerging approaches for the control of soft robot. (a) Embodied intelligence. Reproduced with permission from ref 846. Copyright 2015 American Association for the Advancement of Science. (b) Morphological computation. Reproduced with permission from ref 847. Copyright 2017 Taylor & Francis. (c) Mechanical computing. Reproduced with permission from ref 848. Copyright 2021 Springer Nature. (d) Energy-efficient gait with minimal control by avian-inspired leg clutching. Reproduced with permission from ref 849. Copyright 2022 American Association for the Advancement of Science. (e) Swarm of sterically interacting robots by morphological computation. Reproduced with permission from ref 850. Copyright 2023 American Association for the Advancement of Science. (f) Digital pneumatic logic for soft robotic control. Reproduced with permission from ref 851. Copyright 2024 American Association for the Advancement of Science. \n\nModel-based control offers several advantages for soft robots, including the ability to predict and optimize performance, design controllers tailored to specific tasks, and analyze the effects of different design parameters. However, accurate modeling of the complex mechanics of soft materials remains a significant challenge, requiring careful consideration of nonlinearities, material properties, and environmental factors. \n\n6.2.2. Data-Driven Control. Data-driven control of soft robots involves leveraging experimental or real-world data to develop control strategies without relying heavily on analytical models.830,831 Unlike model-based control, which requires accurate mathematical descriptions of the robot’s dynamics, data-driven control focuses on learning control policies directly from data collected during robot operation. For example, Ju et al. reported a reinforcement learning-based control framework tailored for soft robots with high degrees of freedom, as shown in Figure 45d.832 This framework is designed to enable the execution of global tasks by coordinating the actions of multiple segments equipped with independently controllable embedded actuators. The control policies are formulated leveraging localized proprioceptive self-sensing capabilities, allowing for effective feedback control within each segment (Figure 45e). As demonstration, soft physical robots are developed and deployed in various tasks as expected, thereby validating the effectiveness and applicability of such a datadriven-based control framework in enabling multifunctional, high degrees of freedom soft robots to perform complex tasks. \n\nData-driven control offers several advantages for soft robots, including the ability to adapt to complex and uncertain environments, learn from experience, and handle nonlinear and time-varying dynamics without requiring explicit models.833,834 However, data-driven approaches may require large amounts of training data and careful consideration of issues such as overfitting and generalization. Additionally, interpretability and robustness can be challenges with complex machine learning models. Despite these challenges, data-driven control presents a promising approach for advancing the capabilities of soft robots in real-world applications. \n\n6.2.3. Hierarchical/Hybrid Control. The octopus exhibits a neural architecture that contrasts sharply with the predominantly centralized brain structure found in vertebrates.836,837 Instead of a centralized brain, two-thirds of the octopus’s neural tissue is distributed throughout its arms, primarily responsible for low-level sensorimotor tasks and coordination of whole-arm movements (Figure 45f). In contrast, the central nervous system comprises the remaining third of the neural tissue that is responsible for higher-level functions such as learning and decision-making, integrating signals from the entire body. This hierarchical organization enables the octopus to exhibit complex behaviors and adaptability, showcasing the efficiency and versatility of decentralized control systems in biological organisms. Inspired by this hierarchical structure, Shih and co-workers introduced a hierarchical control/hybrid control framework for the purpose of coordination of multiple soft arms, consisting of three hierarchical levels: high-level decision-making, low-level motor activation, and local reflexive behaviors via sensory feedback.835 As shown in Figure $45\\mathrm{g},$ while central level works for decision making, such as reaching for food or crawling, muscle activations translate incoming commands into suitable deformations at the arm level through a rapid energy-shaping method aimed at reducing energy consumption. Figure 45h describes the architecture of neural network energy shaping control. It should be noted that model-free reinforcement learning in this work is mainly for high-level decision-making and model-based energy shaping for arm-level motor execution. Also, there’re also some proposed hierarchical sensorimotor control frameworks for human-in-the-loop robotic hands.810",
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"category": " Results and discussion"
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"chunk": "# 6.3. Emerging Approaches \n\nIn addition to traditional control strategies for soft robots, such as model-based, data-driven, and hybrid control, emerging techniques like embodied intelligence, morphological computation, and mechanical computing offer significant advantages.25,838−842 Compared to conventional methods, these unconventional approaches have the merits ranging from enhancing energy efficiency, adaptability, and simplifying control by utilizing the robot’s physical interactions with its environment to reduces computational load and improves taskspecific performance and to offer inherent parallelism, greater durability, and seamless integration with soft materials. These unconventional approaches capitalize on the physical properties of soft robots, resulting in more efficient, adaptable, and robust control systems. \n\n6.3.1. Embodied Intelligence. Embodied intelligence means that intelligence is not solely confined to the brain or central processing unit of an agent but is distributed throughout its entire body or structure. In other words, the physical design and material properties of the soft robot contribute significantly to its ability to perceive, interact with, and adapt to its environment.843 \n\nIn soft robotics, the physical embodiment of the robot, typically made from compliant and deformable materials, plays a crucial role in its intelligence. By designing the robot’s body to have properties such as compliance, variable stiffness, and dexterity, it can better navigate complex and dynamic environments. Rather than relying solely on complex algorithms or centralized control systems, soft robots leverage their physical structure to perform tasks efficiently and adaptively. For example, a soft robotic gripper with compliant fingers can conform to the shape of objects it grasps, allowing for more reliable and versatile manipulation.844 Similarly, a soft-bodied robot with variable stiffness can adjust its rigidity to navigate different terrain or interact safely with humans.845 In essence, embodied intelligence in soft robotics emphasizes the integration of sensing, computation, and action within the robot’s physical form, enabling it to exhibit intelligent behavior without the need for extensive programming or external control. \n\nFigure 46a provides a possible architecture of embodied intelligence, with the abilities to seamlessly integrate sensing, computation, and actuation throughout the continuous structure.846 Sensors embedded within the material can detect changes or stimuli across its entire surface, while computing elements process this information locally. Actuators embedded within or connected to the material can then respond to these signals, allowing for distributed and coordinated motion or behavior. This continuous coupling between sensors and actuators at different locations enables soft robots to adapt and interact with their environment in a more fluid and versatile manner. \n\n6.3.2. Morphological Computation. While embodied intelligence provides the overarching framework that encompasses the interaction between the robot and its environment, morphological computation is a specific mechanism through which this interaction occurs, emphasizing the role of the robot’s physical morphology in achieving intelligent behavior.852 In another words, embodied intelligence broadly involves the idea that intelligence emerges from the interaction between an agent (such as a robot) and its environment, with the agent’s body playing a central role in this interaction.850 This includes not only the physical structure of the robot but also its sensory and motor capabilities. Whereas, morphological computation specifically focuses on how the physical morphology of the robot, including its shape, material properties, and mechanical design, can contribute to computational processes. It emphasizes the idea that the robot’s body itself can perform computations or assist in problem-solving tasks, reducing the reliance on centralized control or complex algorithms. Thus, embodied intelligence encompasses the broader framework within which morphological computation operates. Eder and co-workers introduced a morphological computation-based control approach for a highly complex pneumatically driven robotic arm composed of multiple modular segments (Figure 46b).847 By harnessing the dynamics of the robot as a computational resource, the control task is simplified to a straightforward linear regression process, thereby streamlining the control process and enhancing computational efficiency. This work underscores the potential of morphological computation in revolutionizing control strategies for soft robotics, offering a promising pathway toward achieving robust and efficient control in complex robotic systems. \n\n \nFigure 47. (a) Venn diagram of machine learning algorithms learning concepts and classes with model sketch maps. (b) Diagram showing the convergence of AI with soft robots as Soft Robot Agent AI. (c) An overview of the implementation of Soft Robot Agent AI. \n\n6.3.3. Mechanical Computing. While electronic computing offered advantages in miniaturization and integration, recent developments have spurred a reevaluation of mechanical mechanisms in conjunction with materials science and robotics. This interdisciplinary approach opens avenues for novel computing systems that interact with and adapt to their environment, augmenting traditional electronic computing. Yasuda and co-workers gave their insights into the mechanical computing as a new paradigm for soft robotics, and also provided an architecture for such unconventional computing (Figure 46c).848 By leveraging adaptable materials and structures as a distributed information processing network, mechanical computing systems introduce a paradigm where information processing becomes akin to a material property, alongside traditional attributes like strength and stiffness. The acknowledgment of information processing as a material property heralds a transformative shift in computing systems, necessitating a multidisciplinary approach to address the ensuing challenges, including materials science, information theory, computer science, additive manufacturing, and robotics. The framework they provided would serve as a catalyst for innovation, inspiring researchers to explore new avenues in material-driven computation and finally paving the way for groundbreaking advancements in sensorimotor control of soft robotics. \n\nOverall, we’ve delved into three key concepts within the field of soft robotics: embodied intelligence, morphological computation, and mechanical computing. Not independent of each other, each of these concepts offers unique insights into the design and operation of robotic systems, highlighting the importance of the physical embodiment, material properties, and interaction with the environment in achieving intelligent behavior. By integrating these principles, researchers can develop innovative approaches to robotics and control, as the three examples shown in Figure 46d−f, that push the boundaries of traditional methodologies and pave the way for new technological advancements.849−851 It can be envisioned that the weights of these emerging approaches in the control of soft robotic systems will be poised to grow.",
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"category": " Results and discussion"
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},
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{
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"id": 41,
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"chunk": "# 7. ARTIFICIAL INTELLIGENCE (AI) IN SOFT ROBOTS WITH SENSORIMOTOR FUNCTIONS \n\nArtificial intelligence (AI), machine learning (ML), and deep learning (DL) play crucial roles in robotics.853,854 Robots utilize these technologies to perceive and interact with their environment, make decisions, and execute complex tasks.855 Sensing and actuation components are vital for robots to interpret various signals and perform actions such as grasping and manipulation. As a result, a variety of ML and DL techniques are employed in these tasks. The integration of ML, and DL into robotics holds great potential, empowering robots to enhance their intelligence, autonomy, and effectiveness across various applications.856 \n\nWith equal significance, the integration of AI with electronic sensing devices exemplifies humanity’s drive for innovation and efficiency, significantly enhancing sensor capabilities and transforming our interaction with the world.857 AI-enabled sensors are reshaping industries such as healthcare diagnostics and industrial automation by improving data analysis and predictive capabilities. These devices capture diverse inputs\u0001 like strain, pressure, and temperature\u0001using AI for advanced pattern recognition and predictive analysis, yielding diverse outputs from gesture recognition to material property calculations. \n\nThe convergence of AI and soft robotics marks a new era of innovation, overcoming previous limitations and equipping soft robots with cognitive abilities for perception, learning, and intelligent decision-making. This synergy has the potential to revolutionize industries such as healthcare, manufacturing, and exploration by enabling soft robots to navigate uncertainties, adapt to dynamic environments, and perform tasks with precision. AI also enhances control in soft robotics through machine learning and neural networks, allowing adaptive and responsive strategies for diverse environments. Overall, the integration of AI in soft robotics fosters unprecedented advancements, transforming how robots perceive, interact with, and adapt to their surroundings, thereby pushing the boundaries of robotic systems.",
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"category": " Introduction"
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"id": 42,
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"chunk": "# 7.1. Machine Learning Framework \n\nIn the era of the Fourth Industrial Revolution (Industry 4.0), vast amounts of data, including IoT data, cybersecurity data, mobile data, business data, social media data, and health data, are generated. The intelligent analysis of this data and the development of smart, automated applications hinge on the knowledge of AI, particularly, ML.858 This field synthesizes concepts from various disciplines, including artificial intelligence, probability and statistics, computer science, information theory, psychology, control theory, and philosophy.859 Within the realm of machine learning, various techniques such as classification analysis, regression, data clustering, feature engineering, dimensionality reduction, association rule learning, and reinforcement learning are utilized to effectively construct data-driven systems. \n\nML models and Artificial neural networks (including DL) used in robotics are shown in Figure 47a. Machine learning includes basic machine learning methods and artificial neural networks (which can be further classified into shallow neural networks and deep neural networks). Traditional ML algorithms contain SVM (support vector machine), decision tree, kNN (k-nearest neighbor) and Adaboost. Random forest (RF) learning model with multiple decision trees uses “parallel ensembling” which fits several decision tree classifiers in parallel on different data set subsamples and uses majority voting or averages for the outcome or final result. In sensorrelated research, supervised learning methods such as kNN, SVM, and supervised deep learning models are predominantly utilized. These algorithms are primarily employed for classification tasks, enabling the differentiation of various objects upon contact. Unlike the random forest that uses parallel ensembling, Adaboost uses “sequential ensembling”. It creates a powerful classifier by combining many poorly performing classifiers to obtain a good classifier of high accuracy. In DL algorithms, CNNs (convolutional neural networks), RNNs (recurrent neural networks), MLPs (multilayer perceptions), autoencoder, and transformer. CNNs are commonly utilized for image recognition tasks due to their ability to effectively capture spatial patterns. In addition, CNNs are employed for sensors with two-dimensional array data types, such as e-skin. These networks are used for tasks such as object contact classification. RNNs excel in sequential data processing tasks such as speech recognition and natural language processing. Therefore, algorithms, such as RNN and long short-term memory (LSTM), adept at handling timeseries data are frequently used for time-series classification and execution tasks.860 MLPs are versatile neural networks used for various tasks including classification and regression. These algorithms are sensitive to feature scaling and offer various tunable hyperparameters, such as the number of hidden layers, neurons, and iterations, which can lead to computationally expensive models. Autoencoders are unsupervised learning models utilized for feature learning and data compression tasks. Transformers, known for their attention mechanism, have revolutionized natural language processing tasks, particularly in machine translation and text generation. \n\nDifferent machine learning algorithms serve distinct purposes in soft robotics applications. For electronic sensing devices, CNNs excel at processing spatial data from distributed sensor networks and visual feedback, while MLPs are versatile for sensor calibration and multisensor fusion. RNNs demonstrate superior performance in temporal sequence prediction and continuous motion control due to their memory capabilities. The transformer architecture enables complex sequence-to-sequence tasks and simultaneous processing of multiple sensor inputs. Autoencoders are particularly valuable for dimensionality reduction and denoising of highdimensional sensor data. Supporting algorithms like SVMs and decision trees provide efficient solutions for classification tasks and interpretable control decisions. The integration of multiple algorithms often yields the most robust solution: CNNs handle perception tasks, RNNs manage temporal predictions, while lighter architectures like MLPs or decision trees enable realtime control. The selection of specific algorithms depends on the application requirements, such as computational resources, response time, and the complexity of the sensing or control task. In terms of soft robotic control, various machine learning approaches offer unique advantages. Reinforcement learning (RL) has demonstrated remarkable capabilities in learning complex control policies for soft robots through trial-and-error interactions with the environment. Deep RL algorithms can handle the high-dimensional state spaces typical in soft robotics, learning to map sensory inputs directly to actuation commands while adapting to material nonlinearities and environmental uncertainties. Model-based RL approaches are particularly valuable as they can learn dynamic models of the soft robot’s behavior, enabling more efficient policy learning and better generalization to new tasks. For trajectory optimization, RNNs and LSTMs excel at learning continuous motion patterns and predicting deformation behaviors, while MLPs can efficiently map desired states to actuation inputs. Hybrid approaches that combine multiple algorithms have been found to be most effective, as they can leverage the strengths of each algorithm to provide a comprehensive solution for soft robotic tasks. \n\nAs shown in Figure 47b, the convergence of artificial intelligence (AI) and soft robotics represents a transformative shift in the capabilities of robotic systems, driven by the integration of advanced “brains” and adaptive “bodies”. This integration follows a progression through six levels of development: structural, mechanism, movement, perceptual, cognitive, and fully autonomous. At the structural level, the focus lies on designing and optimizing soft robotic bodies, leveraging flexible materials and novel fabrication techniques to mimic biological forms. As the system evolves to the mechanism level, AI is employed to refine actuation and mechanical operations, ensuring seamless interaction between the robot’s components. The movement level emphasizes coordinated and adaptive motion, where AI algorithms enable tasks like locomotion and manipulation, bridging the gap between mechanical design and environmental interaction. The integration deepens at the perceptual level, where AI facilitates advanced sensing capabilities, allowing robots to interpret tactile, visual, and environmental inputs with greater precision. This stage enhances the robot’s responsiveness to external stimuli, enabling effective interaction with dynamic surroundings. Progressing to the cognitive level, AI drives learning, decision-making, and reasoning processes, empowering robots to undertake complex tasks such as navigation, problem-solving, and collaborative operations. Finally, at the fully autonomous level, robots achieve independence, performing intricate tasks with minimal or no human supervision in unstructured and dynamic environments. This continuum reflects a decreasing reliance on human oversight and an increasing capacity for robotic self-regulation, marked by the progressive enhancement of both physical and cognitive functionalities. The integration of AI in this framework not only transforms soft robotics but also unlocks new opportunities for autonomous systems in healthcare, manufacturing, and exploration. As robots evolve into entities with stronger “brains” and perceptive “bodies,” the synergy between AI and soft robotics continues to shape the future of intelligent, adaptable machines. \n\nIn this review, we define such soft intelligent machines as Soft Robot Agent AI. This represents a sophisticated integration of agent-based artificial intelligence with soft robotic systems, combining cognitive intelligence with physical adaptability to enable autonomous and intelligent behavior in complex environments. In this framework, the AI agent functions as a decision-making entity that perceives its environment, processes sensory data, and executes actions to achieve predefined goals. The inherent flexibility and compliance of soft robotic structures enhance this capability, allowing these systems to perform intricate tasks in dynamic and unstructured conditions. The synergy between AI and soft robotics facilitates a seamless interaction between sensing, actuation, and control, enabling robots to autonomously navigate, manipulate, and interact with their surroundings. By leveraging advanced machine learning techniques, such as reinforcement learning and neural networks, Soft Robot Agent AI systems can continuously adapt and optimize their performance based on real-time feedback. The implementation of Soft Robot Agent AI (Figure 47c) involves the integration of artificial intelligence with soft robotic systems, creating an intelligent agent capable of perceiving, learning, and acting autonomously in complex environments. This process begins by embedding multimodal sensory inputs, such as visual, tactile, auditory, and environmental data, into the robot, enabling it to perceive its surroundings in real-time. These inputs are processed through AI algorithms that allow the agent to recognize and interpret stimuli, adapt to changing conditions, and make informed decisions based on its environment. A critical aspect of this implementation is the use of a closed-loop feedback system for continuous learning, where sensory inputs are continuously fed into the AI, allowing it to adjust its actions and improve performance over time. The integration of learning models allows the system to adapt and optimize its decision-making processes through experience. Furthermore, memory systems help the robot store and retrieve knowledge, enabling it to reason and make informed decisions based on past experiences. \n\nBy grounding the agent in both physical and virtual environments, Soft Robot Agent AI can interact with the world in a more robust, context-aware manner. The interaction between perception, cognition, and action enables the robot to respond dynamically and intelligently to environmental changes, while ensuring accuracy and relevancy in decisionmaking. This multilayered, adaptive system enhances the robot’s versatility, making it suitable for diverse applications such as healthcare, human−robot collaboration, and industrial automation.",
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"category": " Results and discussion"
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},
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"id": 43,
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"chunk": "# 7.2. AI for Flexible Electronic Sensing Devices \n\nIn an era marked by the relentless advancement of technology, the marriage of AI with electronic sensing devices stands as a testament to humanity’s quest for innovation and efficienc y.861−865 This amalgamation not only empowers electronic sensors with enhanced capabilities but also revolutionizes the way we perceive and interact with the world around us.866 From the intricacies of healthcare diagnostics to the complexities of industrial automation, AI-infused soft sensing devices are reshaping industries, augmenting human capabilities, and unlocking new realms of possibilit y.862,863,867,868 In this exploration, we delve into the realm of AI-enabled electronic sensing devices, unraveling their transformative potential, applications, and the profound impact they wield on our daily lives and the future of technology. \n\n \nFigure 48. Artificial intelligence for flexible electronic sensing devices. (a) Working flow of electronic sensing devices with AI for signal processing. (b) RF for gas classification. Reproduced with permission from ref 870. Copyright 2022 American Association for the Advancement of Science. (c) kNN for decoding of facial expressions. Reproduced with permission from ref 871. Copyright 2020 Springer Nature. (d) SVM for sign-to-speech translation. Reproduced with permission from ref 872. Copyright 2020 Springer Nature. (e) CNN for learning human grasping and object recognition. Reproduced with permission from ref 67. Copyright 2019 Springer Nature. (f) AdaBoost for monitoring blood pressure. Reproduced with permission from ref 873. Copyright 2022 Springer Nature. (g) GlovePose for hand movement capturing. Reproduced with permission from ref 877. Copyright 2024 Springer Nature. \n\nThe fusion of AI with electronic sensing devices has been regarded as an indispensable approach in the intricate tapestry of modern flexible sensing landscapes. Their true potential lies not merely in data collection, but in the transformative power of AI-driven analysis and more, serving as the catalyst for unlocking the latent insights buried within the deluge of sensory data.869 As the illustrated working flow of AI in electronic devices in Figure 48a, for example, these devices are first designed to capture a myriad of inputs ranging from strain and pressure to temperature and chemical composition. Harnessing the capabilities of AI, these electronic sensing devices transcend mere observation, delving into the realms of pattern recognition and predictive analysis. The outputs generated by this symbiotic relationship between sensor and AI are as diverse as the inputs they process. From the recognition of gestures and textures to the calculation of Young’s Modulus and thermal conductivity, this powerful tool can enhance our understanding of the physical world in unprecedented ways. \n\nThe applications enabled by the marriage of sensory input and AI-driven analysis span a vast spectrum of fields. Capman et al. introduced graphene-based variable capacitor arrays, containing functionalized sensors with different chemical receptors (Figure 48b).870 Powered by random forest classification (RF), gas classification with an accuracy of $98\\%$ can be achieved, which is comparable with analytes. This finding highlights the critical role of analysis methods, particularly machine learning, in noisy environments. In another work, Sun and co-workers employed kNN to decode of facial movements via real-time detection and classification of skin-deformation signatures by conformable piezoelectric thin films, holding promise for nonverbal communication technology and neuromuscular condition monitoring (Figure 48c).871 In addition to facial expression, hand gestures can also be recognized and translated to language assisted by AI. As shown in Figure 48d, Zhou and co-workers demonstrated a wearable sign-to-speech translation system, consisting of yarn-based stretchable sensor arrays and a wireless printed circuit board.872 Features extracted from transmitted data act as inputs for the trained multiclass SVM classifier, which can enhance the accuracy and resilience of the wearable sign-tospeech translation system. Also aiming at hand, Sundaram et al. demonstrated a wearable glove integrated with 548 tactile sensors to learn the signatures of the human grasp, as shown in Figure 48e.67 The integration of a scalable tactile glove with a deep CNN to identify objects, estimate their weight, and analyze tactile patterns during grasping is intriguing. The largescale data set collected from interactions with various objects, together with machine learning techniques could provide valuable insights into human grasping dynamics. \n\n \nFigure 49. Artificial intelligence for soft robotic systems. (a) Working flow of perception robotic systems with inputs of information such as vision, tactile, hearing, and magnetic. Followed by signal processing with AI, a variety of applications can be the enabled as the output of this system. Examples including (b) RNN for soft robotic proprioception. Reproduced with permission from ref 882. Copyright 2019 American Association for the Advancement of Science. (c) CNN for bending perception. Reproduced with permission from ref 883. Copyright 2021 Cambridge University Press. (d) kNN for robotic physicochemical sensing Reproduced with permission from ref 884. Copyright 2022 American Association for the Advancement of Science. (e) CELM for dynamic handling. Reproduced with permission from ref 885. Copyright 2015 Taylor & Francis. (f) Reinforcement learning for robotic adaptation. Reproduced with ref 886. Copyright 2022 Wiley. (g) Transformer for morphological reconstruction. Reproduced with permission from ref 158. Copyright 2023 Springer Nature. \n\nDespite the aforementioned commonly used machine learning techniques, some special approaches are also adopted in the deployment of flexible sensing devices. For example, a different machine learning models with adaptive boosting (AdaBoost) was employed to continuously monitor arterial blood pressure (BP) by Kireev and co-workers (Figure 48f).873 \n\nAdaBoost, as a boosting algorithm, iteratively trains weak learners (in this case, decision trees) to focus on the data points that are difficult to predict, ultimately improving the overall model’s performance.874−876 In this context, AdaBoost is effectively utilized to correlate the extracted features with control BP values. The iterative nature of AdaBoost allows it to select the most informative features and build a strong ensemble model from multiple weak learners. By iteratively adjusting the weights of misclassified data points, AdaBoost ensures that subsequent weak learners focus more on the difficult-to-predict instances, thereby improving the overall predictive accuracy. In another work, Tashakori et al. reported an accurate and dynamic approach for tracking the movement of fingers by a smart glove integrated with sensor yarns and machine learning, as demonstrated in Figure $48\\mathrm{g.}^{877}$ The high dynamic range of the sensor yarns, capable of responding to strains ranging from $0.005\\%$ to $155\\%$ , coupled with stability during extensive use and washing cycles, ensures reliable and durable performance. The use of multistage machine learning techniques (GlovePose) enables accurate estimation of joint angles that rivals that of costly motion-capture cameras, without the limitations of occlusion or field-of-view constraints. This AI-enabled innovative technology has the potential to revolutionize how hand movements are tracked and utilized in various applications, paving the way for more intuitive and immersive human−computer interactions, advanced robotics, and personalized tele-health solutions.",
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"category": " Results and discussion"
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"chunk": "# 7.3. AI for Soft Robotic Systems \n\nIn the realm of robotics, the convergence of AI with soft robotics heralds a new era of innovation and possibility.136,878−880 By leveraging the capabilities of AI, soft robots transcend the limitations of their predecessors, offering solutions to complex challenges while endowing soft robots with cognitive abilities and enabling perception, learning, and intelligent decision-making.881 This amalgamation of AI and soft robotics stands poised to revolutionize industries ranging from healthcare and manufacturing to exploration and beyond. In this discourse, we embark on an exploration of the symbiotic relationship between AI and soft robotics, delving into how AI-driven intelligence empowers soft robots to navigate uncertainty, adapt to dynamic environments, and perform tasks with precision and efficiency. From advanced control algorithms to machine learning and neural networks, we unravel the transformative potential of AI in shaping the future of soft robotics, pushing the boundaries of what is achievable in robotic systems. \n\nIn the integrated workflow of AI within a soft robotic system, sensory inputs (such as vision, tactile, hearing, etc.) are first processed to perceive and map the environment, enabling accurate localization and object detection, as the example illustrated in Figure 49a. AI algorithms then make decisions based on this information, determining optimal control strategies for the robot’s actions, which can include navigation, manipulation, recognition, action planning, shape adaptation and so on. Through continuous learning and adaptation, the system refines its behaviors over time, leveraging feedback to improve performance and adapt to changing conditions. In scenarios involving human interaction, AI facilitates seamless communication and collaboration, ensuring safe and effective engagement. This iterative process enables soft robots to autonomously perceive, learn, adapt, and interact with their surroundings intelligently, opening up diverse applications across industries. \n\nPerception serves as a foundational pillar within the realm of intelligent autonomous systems, essential for facilitating closedloop control and the accurate representation of the surrounding environment. Within the context of traditional rigid robotics, the attainment of robust perception has been facilitated by the deployment of highly specialized sensors meticulously arranged to capture both proprioceptive and exteroceptive information with high fidelity. However, the emergence of soft robotics with inherent modeling complexity and nonlinear challenges in soft materials introduces a paradigm shift, presenting a new hurdle to the development of soft robotics. This complexity introduces ambiguity in the design, integration of soft sensors, and modeling, fabrication, and control of soft robotics, further amplifying the intricacies of perception within the realm of soft robotics. To address this issue, Thuruthel et al. proposed a solution for modeling unknown soft actuated systems.882 Specifically, a redundant and unstructured sensor within a soft actuator alongside a vision-based motion capture system for ground truth are employed in this system, as shown in Figure 49b. Demonstrating real-time kinematic modeling of soft continuum actuators while effectively handling sensor nonlinearities and drift, this innovative approach facilitates the development of force and deformation models as well as the integration of action and perception in robotic systems. Also utilizing visual information, Zhang and co-workers introduced another perceptive soft robotic finger, which comprises a colored soft inner chamber, an outer structure, and an endoscope camera (Figure 49c).883 The bending perception of the soft finger relies on the images that are prepossessed by deep learning techniques, and CNN is trained to discern the bending states of the finger. \n\nWhile most reported robotic sensing technologies have mainly emphasized monitoring physical parameters such as pressure, strain and temperature, the incorporation of other sensing modalities with multimodal perception abilities is comparatively less. For example, robots integrated with chemical sensors for autonomous dry-phase analyte detection could be of significance in agriculture, security, environmental protection, and public health, which presents a particularly daunting challenge that remains largely unexplored. In accordance with this, Yu et al. demonstrated an AI-powered multimodal robotic sensing system that can not only detect electrophysiology signals and tactile information, but a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens (Figure 49d).884 In their work, a multimodal large-area eskin was fabricated by inkjet printing of custom-developed nanomaterial inks, which could significantly advance robotic capabilities by enabling them to detect and react to stimuli in their surroundings, thereby enhancing their autonomy and decision-making prowess. \n\nApart from sensing in soft robotics, AI also plays a significant role in the precise control of them, a task critical for their effective operation in diverse and dynamic environments. Through the application of AI techniques like machine learning and neural networks, soft robots can achieve adaptive and responsive control strategies autonomously, enhancing efficiency and robustness in completing tasks. Figure 49e demonstrates a bionic handling assistant empower by constrained extreme learning machine (CELM) for dynamic handling of objects and interaction.885 This approach not only demonstrates the seamless integration of specific operation modes and varying levels of control for soft continuum robots, but more importantly, serves as a blueprint for control design across similar platforms. Behrens et al. presented another control scheme for smart helical magnetic hydrogel microrobot by leveraging the soft actor-critic reinforcement learning algorithm to derive a control policy, as illustrated in Figure 49f.886 The control policies involved in this work are learned by the reinforcement learning agent from both state vector input and raw images, with the behavior of the agent recapitulating that of rationally designed physical modelbased controllers microrobots. This application of deep reinforcement learning in the control of microrobots holds endless promise for significantly enhancing the boundaries of future microrobot generations. By leveraging transformer, soft robots can efficiently process complex sensory inputs, such as tactile and visual data, enabling them to better understand their environment and make informed decisions in real-time. Based on transformers, Hu and co-workers introduced an intelligent stretchable capacitive e-skin for soft robots with high proprioceptive geometry resolution (3900), achieving their morphological reconstruction with high fidelity, as shown in Figure 49g.158 The deformation of soft robot can be measured by capacitive sensors that are integrated throughout its body, followed by translating these deformation information into high-density point clouds representing the complete geometry using a deep architecture based on transformers. Benefitting from this approach, issues regarding with high density proprioceptive geometry resolution on soft robots can be addressed, holding promise for the unprecedent advancements a in soft robotics, including precise closed-loop control, digital twin modeling, etc. \n\n \nFigure 50. Application of soft robot for exploration. (a) Wind-dispersed wireless sensing devices, inspired by plant seed dispersal mechanisms. (b) Photograph of the 3D microfliers inspired by seeds. (c) Exploded view of our origami microflier displaying its key components and Outdoor drop tests. (d) Illustration depicting drone flight showcasing avian-inspired wing and tail morphing abilities. (e) Image showing the burrowing soft robot for subterranean navigation. (f) Image depicting the robophysical model inspired by Caenorhabditis elegans navigating a pile of rocks. (g) Image depicting the bioinspired mouse robot navigating a maze, showcasing lateral flexion enabled by its compliant spine. (h) Imaging depicting the core components of the Exobiology Extant Life Surveyor (EELS) robot. (i) Illustration depicting the design of the soft robot and its field test in the South China Sea. (j) Illustration of the design of the soft robotic fish (Sofi) and underwater exploration. (k) Illustration of the lionfish-inspired robot with multifunctional zinc iodide redox flow batteries for power. (l) Image of the custom soft manipulators in operation within the deep-sea environment. $\\mathrm{(m)}$ A turtle-inspired terrestrial−aquatic robot. (n) A terrestrial−aerial hybrid robot. (o) An aerial−aquatic hybrid robot inspired by the remora fish. (a) Reproduced with permission from ref 902. Copyright 2022 Springer Nature. (b) Reproduced with permission from ref 905. Copyright 20221 Springer Nature. (c) Reproduced with permission from ref 903. Copyright 2023 American Association for the Advancement of Science. (d) Reproduced with permission from ref 904. Copyright 2020 American Association for the Advancement of Science. (e) Reproduced with permission from ref 906. Copyright 2021 American Association for the Advancement of Science. (f) Reproduced with permission from ref 907. Copyright 2023 American Association for the Advancement of Science. (g) Reproduced with permission from ref 908. Copyright 2023 American Association for the Advancement of Science. (h) Reproduced with permission from ref 909. Copyright 2024 American Association for the Advancement of Science. (i) Reproduced with permission from ref 910. Copyright 2021 Springer Nature. (j) Reproduced with permission from ref 911. Copyright 2018 American Association for the Advancement of Science. (k) Reproduced with permission from ref 159. Copyright 2019 Springer Nature. (l) Reproduced with permission from ref 912. Copyright 2018 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. (m) Reproduced with permission from ref 913. Copyright 2022 Springer Nature. (n) Reproduced with permission from ref 914. Copyright 2023 Cambridge University Press. (o) Reproduced with permission from ref 915. Copyright 2022 American Association for the Advancement of Science.",
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"chunk": "# 8. APPLICATIONS \n\nSoft robotics has emerged as a transformative field with vast application scopes across various industries. From exploration in challenging terrains to revolutionizing healthcare, from enhancing immersive experiences in XR/AR/VR to enabling delicate object manipulation, soft robotics offers unprecedented versatility and adaptability.887−896 In exploration, soft robots navigate through rugged landscapes and confined spaces with agility and resilience, advancing missions in space, deep sea, and disaster zones. In healthcare, they facilitate minimally invasive surgeries, prosthetics, and assistive devices with gentle interaction and high dexterity, enhancing patient care and rehabilitation. Moreover, soft robotics enhances XR/AR/VR experiences by providing realistic haptic feedback and intuitive interactions, elevating immersive simulations and training programs. Furthermore, in object manipulation, soft robots excel in delicate tasks, such as handling fragile items or gripping irregularly shaped objects, offering precision and flexibility in industrial and manufacturing processes.897 Overall, the application scopes of soft robotics are diverse and expanding, offering innovative solutions to complex challenges across industries.",
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"category": " Results and discussion"
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},
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"chunk": "# 8.1. Exploration \n\nSoft robotic exploration encompasses a dynamic field where flexible and adaptable robots are deployed across diverse environments, including aerial, terrestrial, and aquatic domains.893,898−901 T hese robots, characterized by their compliant and deformable structures, offer unique advantages such as enhanced maneuverability, resilience to environmental obstacles, and the ability to traverse challenging terrains. In aerial exploration, soft robots navigate through the skies with agility, accessing remote or hazardous locations where traditional rigid-bodied robots struggle to operate. Terrestrial exploration involves the deployment of soft robots on land, enabling them to traverse rugged landscapes, negotiate obstacles, and interact safely with the environment. In aquatic exploration, soft robots excel in navigating underwater environments, offering precise control and maneuverability while minimizing disturbances to delicate ecosystems. Overall, soft robotic exploration presents a versatile and promising approach to uncovering new frontiers and gaining insights into various natural and man-made environments. \n\n8.1.1. Aerial. Gollakota’s group pioneered the development and testing of wind-dispersed battery-free wireless sensing devices. Drawing inspiration from plant seed dispersal mechanisms, they engineered millimeter-scale devices weighing a mere $30~\\mathrm{mg}$ . Powered by lightweight solar cells and featuring a backscatter communication link, these innovative devices represent a leap forward in autonomous sensing technology. Leveraging dandelion-inspired structures for wide-area dispersal and upright landing, they demonstrated remarkable capabilities, traveling distances of $50-100\\mathrm{~m~}$ in gentle to moderate breezes (Figure 50a).902 Similarly, Roger’s group has also developed wind-dispersed, battery-free wireless devices inspired by seeds (Figure 50b). Through mechanically guided assembly techniques, they’ve created miniature 3D fliers for diverse applications in environmental monitoring and surveillance. These fliers, spanning various scales, incorporate active electronic and colorimetric payloads. Their innovative approach combines bioinspired design principles with analytical, computational, and experimental studies of aerodynamics, paving the way for a wide range of practical applications. Gollakota et al. engineered solar-powered origami microfliers capable of altering their shape midair to control dispersal distance. Employing bistable leaf-out origami structures and a low-power actuator, these microfliers integrated a range of components, including a microcontroller, Bluetooth radio, and sensors for environmental data collection. Demonstrating impressive capabilities, they traveled up to 98 m in a gentle breeze, transmitting data wirelessly over $60\\mathrm{~m~}$ (Figure 50c).903 Floriano developed LisHawk, a drone featuring morphing wings and tails inspired by avian flight (Figure 50d).904 Through wind tunnel experiments, morphology optimization, and flight tests, they assessed its performance, noting enhanced maneuverability and agility in both standard and aggressive flight. \n\n8.1.2. Terrestrial. For terrestrial exploration, Naclerio et al. investigated subterranean locomotion, devising a soft burrowing robot.906 Testing three hypotheses regarding interaction forces in granular media, they validated them through experiments. The robot successfully burrowed through sand in different paths, horizontally and vertically (Figure 50e). Goldman’s group studied limbless locomotion using both biological and robophysical models, focusing on the nematode Caenorhabditis elegans. They developed a snake-like robot model to emulate its movement, aiming to explore the role of mechanical intelligence in navigating complex environments (Figure 50f). Comparing C. elegans’ locomotion kinematics to the robophysical model, they discovered that mechanical intelligence simplifies control and enables effective navigation in diverse terrains.907 Bing et al. introduced NeRmo, a bioinspired robotic mouse equipped with a flexible spine mirroring the musculoskeletal structure of actual mice.908 This innovative design enhances NeRmo’s locomotive capabilities, enhancing static stability, walking speed, and maneuverability (Figure 50g). Vaquero et al. reported the snake-like robot called Exobiology Extant Life Surveyor (EELS) tailored for exploration on Enceladus, Saturn’s icy moon (Figure 50h).909 They discussed mobility strategies, task planning modules, and shared insights from both laboratory and field testing phases, outlining limitations and lessons learned along the way. \n\n \nFigure 51. Application of soft robot for healthcare. (a) Photographs displaying the components of the exosuit. (b) Photographs showing the textile pneumatic muscle prototypes for upper limb active suit applications. (c) Photograph showing the exoskeleton system based on sample efficient active learning. (d) Illustration of the passive exosuit with body-powered variable impedance. (e) Photograph showing the components of the 3Dprinted soft robotic hand with multiarticulating capabilities. (f) Photograph of a transradial amputee demonstrating the soft robot hand controlled by both myoelectric signals and tactile feedback. (g) An illustration depicting the third thumb mounted on the side of the palm, augmenting neural body representation. (h) Photograph of an individual wearing the neuromusculoskeletal prosthesis to perform daily tasks. (i) In vivo demonstration of the soft robotic sleeve providing cardiac assist in a porcine model of acute heart failure. (j) Illustration of the robotic right ventricle (RRV) recording invasive hemodynamics in vivo. (k) Illustration depicting the tester wearing the laser-induced graphene (LIG) artificial throat. (l) Illustration of the magnetic soft robotic bladder (MRB) assisted urination. (a) Reproduced with permission from ref 920. Copyright 2019 American Association for the Advancement of Science. (b) Reproduced with permission from ref 921. Copyright 2018 Taylor & Francis. (c) Reproduced with permission from ref 922. Copyright 2023 American Association for the Advancement of Science. (d) Reproduced with permission from ref 923. Copyright 2021 American Association for the Advancement of Science. (e) Reproduced with permission from ref 924. Copyright 2020 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. (f) Reproduced with permission from ref 925. Copyright 2023 Springer Nature. (g) Reproduced with permission from ref 926. Copyright 2021 American Association for the Advancement of Science. (h) Reproduced with permission from ref 927. Copyright 2023 American Association for the Advancement of Science. (i) Reproduced with permission from ref 928. Copyright 2017 American Association for the Advancement of Science. (j) Reproduced with permission from ref 929. Copyright 2023, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (k) Reproduced with permission from ref 930. Copyright 2017, The Authors, published by Springer Nature. Reproduced under the terms of the Creative Commons Attribution 4.0 International License. (l) Reproduced with permission from ref 931. Copyright 2022, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). \n\n8.1.3. Aquatic. For aquatic exploration, Li et al. conducted field tests in the Mariana Trench and the South China Sea to evaluate the performance of a self-powered soft robot designed for deep-sea exploration (Figure 50i). Utilizing high-voltage square-wave a.c. voltages for actuation, the robot demonstrated sustained flapping motion for $45\\mathrm{min}$ powered by a lithium-ion battery. To safeguard electronics from pressure, they were integrated into a silicone matrix and distributed to mitigate shear stress. Experimental and theoretical analyses confirmed the pressure resilience of both electronic components and soft actuators, showcasing the promise of lightweight, soft devices for extreme deep-sea exploration.910 Katzschmann et al. introduced SoFi, an untethered soft-bodied robotic fish, designed for aquatic exploration.911 Controlled remotely by a human diver through an acoustic communication modem, SoFi navigates three-dimensionally, capturing underwater footage and studying aquatic life (Figure 50j). It boasts autonomous depth control via dive planes and a compact buoyancy control unit, alongside an underwater remote control system facilitated by a miniaturized acoustic communication module. Shepherd’s group pioneered a robotic system driven by a liquid-infused battery, inspired by the energy storage concept of redox flow batteries (Figure 50k).159 Utilizing zinc iodide and distilled water, the battery cells were constructed with RFB components, offering a high energy density akin to circulatory systems. This integrated design merges hydraulic force transmission, actuation, and energy storage, resulting in a system energy density of $53\\mathrm{~J~g^{-1}}$ . Demonstrating remarkable endurance, the robot achieves prolonged underwater swimming at a speed of 1.56 body lengths per minute, showcasing the efficacy of this innovative approach through underwater demonstrations. Vogt et al. developed custom soft robotic manipulators for deep-sea exploration and interaction with marine organisms.912 Tested at depths of up to $2224\\mathrm{m}$ using a remotely operated vehicle (ROV) in the Phoenix Islands Protected Area, they enabled the study of various soft-bodied and fragile marine life forms. Instant feedback from ROV pilots and biologists allowed for swift redesign and fabrication of manipulators at sea, including the addition of features like “fingernails” for improved grasping (Figure 50l). \n\n8.1.4. Cross-Media. Kramer-Bottiglio’s group addressed the challenge of designing mobile robots capable of traversing multiple environments effectively. Traditional approaches, such as biomimetic design or adding unique propulsive mechanisms, often result in either suboptimal performance or energyinefficient designs. To overcome these limitations, the researchers implemented “adaptive morphogenesis,” a design strategy inspired by terrestrial and aquatic turtles. This strategy involves integrating rigid components and soft materials to enable the robot to adapt its morphology and behaviors for specialized locomotion across terrestrial, aquatic, and transitional zones. The resulting robot demonstrates improved efficiency through the interplay of gait, limb shape, and environmental medium, highlighting the efficacy of adaptive morphogenesis for enhancing multienvironmental locomotion in robots (Figure $50\\mathrm{m}\\overline{{}},$ ).913 Zhang et al. introduced the autonomous quadrotor tilting hybrid robot (AQT-HR), a terrestrial−aerial hybrid robot integrating flying and driving functionalities. The robot employs a quadrotor for flight and a tilting mechanism for ground locomotion, facilitating autonomous mode switching (Figure 50n).914 Wen’s group developed an innovative aerial-aquatic hitchhiking robot capable of flying, swimming, and adhering to surfaces in both air and water, inspired by the adhesive method of the remora fish (Figure 50o).915 Its hitchhiking capability enables resting on stationary surfaces or attaching to moving hosts, prolonging operational time and expanding monitoring range.",
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"chunk": "# 8.2. Healthcare \n\nSoft robotics has emerged as a transformative field, offering innovative solutions across various domains, including exoskeleton, prosthetics and artificial organs.891,916−918 In the realm of exoskeleton prosthetics, soft robotics introduces novel approaches that prioritize comfort, flexibility, and natural movement, mirroring the characteristics of human muscles and tendons. These advancements aim to enhance mobility and quality of life for individuals with mobility impairments by providing lightweight, adaptable, and intuitive prosthetic devices. 919 \n\n8.2.1. Exoskeletons. Kim’s group has developed a lightweight and portable exosuit designed to assist with hip extension during both walking and running.920 This innovative exosuit automatically adjusts its actuation profiles based on the wearer’s estimated potential energy fluctuations, seamlessly transitioning between walking and running modes. Moreover, the exosuit exhibited versatility by effectively reducing metabolic rates across different running speeds and even uphill walking scenarios (Figure 51a). Belforte et al. developed textile pneumatic muscle prototypes intended for integration into active suits for upper limb rehabilitation (Figure 51b).921 These prototypes, constructed from natural latex and fabric tubes, underwent rigorous pressure−length and pressure−force measurements. Rouse’s group introduced an innovative active learning approach to fine-tune the control parameters of ankle exoskeletons based on user preferences (Figure 51c).922 Their method employed a neural network model coupled with an evolutionary algorithm to rank suggestions for parameter adjustments efficiently. Through simulations and experiments involving human participants, the algorithm demonstrated an impressive average accuracy of $88\\%$ in optimizing control parameters. Cho’s group developed a body-powered variable impedance exosuit designed to optimize lifting posture and reduce the risk of injury during object handling tasks (Figure 51d). The suit utilizes artificial biarticular tendons positioned strategically behind the wearer’s back, hip, and knee joints to create a force field that promotes squatting while hindering stooping. Moreover, metabolic rate decreased during squatting, and compression force on the lumbosacral joint was reduced, indicating enhanced biomechanical efficiency and reduced strain. The immediate motor adaptation observed suggests the potential of the exosuit as an assistive device for training individuals in safer lifting practices.923 \n\n8.2.2. Prosthetics. Mohammadi designed X-Limb, a soft robotic prosthetic hand, which underwent comprehensive evaluation covering mechanical, morphological, kinodynamic, functional, and durability aspects.924 X-Limb emerges as a lightweight, durable, and functional prosthetic hand meeting essential mechanical and kinodynamic requisites while offering practical usability (Figure 51e). Capable of executing various grasp types, X-Limb caters to individuals with upper limb loss, offering versatility and adaptability. Notably, open-source files enable customization and cost-effective fabrication. Utilizing 3D printing technology, X-Limb integrates membraneenclosed flexure joints, synergy-based thumb motion, and a cable-driven actuation system. Zhao’s group engineered a soft neuroprosthetic hand tailored for individuals with transradial amputations (Figure 51f). Boasting six degrees of freedom, this innovation is orchestrated via myoelectric signals from four EMG sensors, coupled with five elastomeric capacitive sensors on the fingertips, delivering tactile feedback. Leveraging 3D printing and cost-effective materials, fabrication encompasses assembly, yielding a meticulously crafted device. Comparative analysis against a conventional rigid neuroprosthetic hand revealed the soft counterpart’s remarkable advantages in speed and dexterity. Moreover, the study delves into the analytical model and finite-element simulations, dissecting pneumatic response dynamics and bending angles of flexible joints within the hand.925 Interestingly, Makin’s group initiated a study examining the impact of hand augmentation using an additional robotic thumb on body representation (Figure \n\n \nFigure 52. Application of soft robot for healthcare. (a) SEM images showcasing the binding between the Janus platelet micromotors (JPL-motor, green) and E. coli (red). (b) Images illustrating how the FSDSR utilizes soft robotic actuations to control drug delivery. (c) In vivo tracking and navigation of a microswarm in the femoral vein of the rat guided by laser speckle contrast imaging (LSCI). (d) Illustration depicting the design of the autonomous robotic catheter (e) In vivo navigation of dexterous helical magnetic robot. (f) Robotic embolization in a rabbit blood vessel in vivo using Magnetic soft microfiberbots. $(\\mathbf{g})$ photographs showing the in vivo acute recording of somatosensory evoked potentials (SSEPs) using an electrocorticography system with a soft robotic actuator. (h) Photographs demonstrating the water-induced shape-adaptive implantation of a circular WRAP electrode on the surface of a rat heart. (i) In vivo experimentation showcasing the use of magnetoelectric nonlinear metamaterial (MNM) for neural stimulation. (a) Reproduced with permission from ref 932. Copyright 2020 American Association for the Advancement of Science. (b) Reproduced with permission from ref 933. Copyright 2023 American Association for the Advancement of Science. (c) Reproduced with permission from ref 934. Copyright 2024 American Association for the Advancement of Science. (d) Reproduced with permission from ref 935. Copyright 2019 American Association for the Advancement of Science. (e) Reproduced with permission from ref 940. Copyright 2024 American Association for the Advancement of Science. (f) Reproduced with permission from ref 936. Copyright 2024 American Association for the Advancement of Science. (g) Reproduced with permission from ref 937. Copyright 2023 American Association for the Advancement of Science. (h) Reproduced with permission from ref 938. Copyright 2023 Springer Nature. (i) Reproduced with permission from ref 939. Copyright 2024 Springer Nature. \n\n51g). Through a longitudinal experimental setup involving 36 healthy volunteers, randomly assigned to augmentation or control groups, the study assessed various parameters such as wear time, pressure sensor data, task performance, numerical cognition, hand kinematics, embodiment questionnaires, and neuroimaging scans. The findings revealed significant alterations in body representation, including enhanced hand motor control, finger coordination, and a sense of embodiment toward the extra thumb. This research highlights how motor augmentation can induce brain plasticity and reshape body perception.926 Ortiz-Catalan et al. detailed the clinical implementation of a transradial neuromusculoskeletal prosthesis, involving titanium implants in the radius and ulna bones, electromuscular constructs, and electrodes in muscles and nerves (Figure 51h). Assessment revealed enhanced prosthetic function, reduced postamputation pain, and improved quality of life. The study underscores the stability of the neuromusculoskeletal interface, sensory feedback via direct neural stimulation, and prosthesis control and signal quality, aiming to elevate the functionality and well-being of individuals with upper limb amputations.927 \n\n8.2.3. Artificial Organs. Roche et al. developed a soft robotic sleeve for cardiac ventricular assist, designed to mimic natural heart motion with linear contractile elements.928 Customizable for patient-specific needs, the sleeve serves as a bridge to transplant for heart failure patients. In vitro and ex vivo experiments optimized the design, followed by testing in pig cadavers and live pigs with acute heart failure (Figure 51i). Results showed increased ejection output and reestablished cardiac output, with the device conforming to the heart surface, synchronizing with native motion, and displacing physiological fluid volumes. Inflammation was mitigated at the device-tissue interface using hydrogel, highlighting the potential of soft robotics in supporting heart function. In addition, Singh et al. presented the development and validation of the robotic right ventricle (RRV), a hybrid soft robotic platform designed to emulate the physiological and mechanical characteristics of the right ventricle (RV) of the heart (Figure 51j). This innovative system integrates a chemically treated endocardial scaffold with a soft robotic synthetic myocardium, effectively replicating RV biomechanics and hemodynamics. Through in vivo studies with porcine models, the RRV demonstrated promise for applications in tricuspid valve repair and replacement.929 Tao’s group introduced an innovative artificial throat leveraging laser-induced graphene (LIG) technology, capable of both sound generation and detection. Fabricated through a singlestep laser writing process on a polyimide film, the LIG throat exhibited a broad frequency range ( $100~\\mathrm{Hz}$ to $40\\ \\mathrm{kHz}$ ) for sound generation, with adjustable sound pressure levels by varying LIG thickness. Moreover, it effectively detected diverse throat vibrations like coughs, hums, and screams, converting them into controllable sounds, while showcasing voice recognition capabilities (Figure 51k). With promising potential in aiding individuals with disabilities, voice control systems, and wearable electronics, the LIG artificial throat marks a significant advancement in human−machine interaction technology.930 Zang’s group developed and validated an implantable magnetic soft robotic bladder (MRB) for assisting urination in individuals with underactive bladders (UABs) (Figure 51l). By applying mechanical compression to the UAB using magnetic fields, the MRB enables on-demand contraction of the detrusor muscle. Testing in a porcine model demonstrated successful urination with increased pressure and fast urine flow, indicating the potential of MRB technology as a promising therapeutic strategy for UABs in humans, with improved efficacy and fewer adverse effects compared to existing methods.931 \n\n8.2.4. Drug Delivery. Soft robotics and actuators have emerged as promising tools for drug delivery applications. Tang et al. introduced Janus platelet micromotors (JPLmotors) by modifying natural platelet cells with asymmetrically immobilized urease enzymes (Figure 52a). This modification enables enhanced chemophoretic motion of the platelets through the decomposition of urea in biofluids. Importantly, this modification preserves the platelets’ inherent biofunctionalities, including their ability to effectively target cancer cells and bacteria. The JPL-motors demonstrate efficient propulsion in the presence of urea fuel, resulting in improved binding efficiency with biological targets. Furthermore, when loaded with model anticancer or antibiotic drugs, they exhibit enhanced therapeutic efficacy.932 Duffy’s group introduced the fibrosensing dynamic soft reservoir (FSDSR), an implantable soft robotic drug delivery device capable of monitoring the foreign body response and adjusting its actuation regimen to counter the effects of fibrotic capsule formation.933 The FSDSR incorporates a FibroSensing membrane to detect changes in electrical impedance, enabling real-time monitoring of fibrotic capsule formation. In vitro tests using Matrigel and myofibroblast proliferation, as well as in vivo experiments in a rodent model, demonstrated the FSDSR’s ability to modulate the foreign body response, enhance drug release, and show potential for closed-loop drug delivery (Figure 52b). Zhang’s group presented a method for real-time tracking and navigation of a magnetic microswarm within biological vascular systems using laser speckle contrast imaging (LSCI) (Figure 52c). The study involved developing a magnetic microswarm system utilizing $\\mathrm{Fe}_{3}\\mathrm{O}_{4}@\\mathrm{PDA}@\\mathrm{Au}$ nanoparticles, controlled by a rotating magnetic field. Experiments were conducted across various environments, including flat surfaces, artificial blood vessel phantoms, ex vivo human placenta, and in vivo rat femoral vein. The research showcased LSCI’s capability to visualize and track the microswarm in realtime, highlighting its potential for biomedical applications like targeted drug delivery in complex environments.934 \n\n8.2.5. Catheters. Dupont’s group investigated the potential of autonomous catheter navigation for intracardiac procedures using a robotic catheter and haptic vision sensor.935 They designed and built the robotic catheter and sensor, developed control algorithms, and optimized the catheter design (Figure 52d). In vivo experiments on animals tested the system’s navigation and task performance, such as leak closure and occluder deployment. Results indicated comparable or improved performance compared to hand-held or teleoperated methods, showcasing the promise of autonomous robotic systems in intracardiac procedures. Introducing a helical magnetic continuum robot $\\operatorname{\\Pi}({\\mathrm{mCR}})$ for remote magnetic navigation in the vasculature, Nelson’s group presents an innovative approach (Figure 52e). Actuated by an electromagnetic navigation system (eMNS), the $\\mathbf{\\Pi}_{\\mathrm{mCR}}$ comprises a torquable backbone, a magnetic tip, and an advancer unit. Through experiments in vitro and in vivo, the efficacy of the helical locomotion principle was assessed, demonstrating successful navigation through blood vessels with minimal damage to the vessel wall.115 Zang and colleagues conducted a comprehensive study on magnetic soft microfiberbots for endovascular intervention (Figure 52f). The fabrication process involves blending ferromagnetic particles with a soft elastomeric matrix to form magnetic microfibers of varying diameters, which are then shaped into helical structures. In vitro and ex vivo experiments showcased the microfiberbots’ deployment, steerability, and embolization capability. Further evaluations included assessments of biocompatibility and functionality. The findings underscore the promising clinical application of these microfiberbots for robotic embolization in submillimeter regions.936 \n\n8.2.6. Surgical Tools. Lacour’s group developed a soft, deployable electrocorticography (ECoG) device for recording brain activity.937 This device features a flexible array of microelectrodes that can be implanted on the brain’s surface (Figure ${\\mathfrak{s}}2{\\mathfrak{g}},\\quad$ ). A significant innovation is its deployable design, facilitating insertion through a small burr hole and subsequent expansion to cover a larger brain area. In vitro and in vivo experiments confirmed its ability to record high-quality neural signals over several weeks. Chen’s group developed a novel wrap electrode array (WRAP-electrode array) for neural interfacing applications.938 The fabrication process involved preparing WRAP films using PEG, PEO, and $\\alpha$ -CD, followed by depositing Au patterns on the WRAP film. Mechanical properties, structure, and water responsiveness of the WRAP films were characterized. The WRAP-electrode array was evaluated for electrical and electrochemical properties, in vitro and in vivo biocompatibility, and functionality in nerve stimulation and electrophysiological signal recording (Figure 52h). The research demonstrates the potential of WRAP films as versatile and biocompatible materials for soft and conformal electrode applications in neural and cardiac systems. Robinson’s group developed and characterized self-rectifying magnetoelectric metamaterials for remote neural stimulation and motor function restoration.939 They engineered a composite material, termed self-rectifying magnetoelectric nonlinear metamaterial (MNM), by incorporating a nanoscale rectifying electron transport (RET) layer into an existing magnetoelectric (ME) laminate. The study demonstrated that MNM could wirelessly stimulate peripheral nerves in anesthetized rats, restoring sensory reflex and signal propagation in a severed nerve with latencies of less than 5 ms (Figure 52i). Additionally, the research included fabrication and characterization of magnetostrictive-electrostrictive (ME) composites and magnetostrictive-nanomagnetic (MNM) composites, along with in vivo experiments assessing the biocompatibility of MNM composites and their ability to stimulate the sciatic peripheral nerve in rats. \n\n \nFigure 53. Application of soft robot for sensory feedback. (a) Untethered pneumatic glove for multimode haptic feedback. Reproduced with permission from ref 942. Copyright 2023 Wiley. (b) Cutaneous tactile force feedback. Reproduced with permission from ref 943. Copyright 2014 ACM. (c) Wearable haptic display. Reproduced with permission from ref 944. Copyright 2007 ACM. (d) Wearable haptics with electrostatic actuators. Reproduced with permission from ref 480. Copyright 2020 Wiley. (e) Wireless self-sensing and haptic-reproducing electronic skin. Reproduced with permission from ref 945. Copyright 2022 American Association for the Advancement of Science. (f) Untethered feel-through haptics with elastomer actuators. Reproduced with permission from ref 946. Copyright 2020 Wiley. (g) Stretchable skin-like thermal device. Reproduced with permission from ref 947. Copyright 2020 Wiley. (h) Stretchable and transparent metal nanowire heater. Reproduced with permission from ref 948. Copyright 2015 Wiley. (i) Soft robotic glove for rehabilitation. Reproduced with permission from ref 949. Copyright 2014 Elsevier. (j) Haptic glove using tendon-driven soft robotic mechanism. Reproduced with permission from ref 950. Copyright 2020 Baik, Park, and Park. (k) A kinesthetic haptic device for index finger. Reproduced with permission from ref 951. Copyright 2022 Springer Nature. (l) Haptic glove integrating with actuators on top of the hand and fingers. Reproduced with permission from ref 952. Copyright 2022 Wiley.",
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"chunk": "# 8.3. Extended Reality (XR: AR/VR/MR) \n\nIn the realm of soft robotics, particularly in the domain of sensory feedback, recent developments are poised to revolutionize human−computer interaction (HCI) by providing users with not only tactile sensations but also kinesthetic information. Spanning from virtual reality (VR) environments to augmented reality (AR) settings, these breakthroughs represent a critical advancement in merging robotics, HCI, and immersive technologies, heralding a future where tactile interactions seamlessly bridge the digital and physical worlds.941 Soft robotics has emerged as a groundbreaking field with vast potential to reshape human−machine interaction (HMI) within extended reality (XR) environments. By leveraging advancements in materials science, sensor technology, and human−computer interfaces, these innovations are paving the way for a more interconnected and interactive future in virtual and augmented realities. \n\n \nFigure 54. Application of soft robots for XR and human−machine interaction. (a) Active mechanical haptics with high-fidelity perceptions. Reproduced with permission from ref 954. Copyright 2023 Springer Nature. (b) Super-resolution wearable electrotactile rendering system. Reproduced with permission from ref 955. Copyright 2020 American Association for the Advancement of Science. (c) Soft and wireless olfactory interface. Reproduced with permission from ref 956. Copyright 2023 Springer Nature. (d) Skin-integrated wireless haptic interfaces. Reproduced with permission from ref 957. Copyright 2019 Springer Nature. (e) A portable force feedback origami robot. Reproduced with permission from ref 958. Copyright 2019 Springer Nature. (f) A wireless haptic interface for programmable patterns of touch. Reproduced with permission from ref 959. Copyright 2022 Springer Nature. (g) Electronic skin as wireless human−machine interfaces. Reproduced with permission from ref 960. Copyright 2022 American Association for the Advancement of Science. (h) Human−robot facial coexpression. Reproduced with permission from ref 961. Copyright 2024 American Association for the Advancement of Science. (i) Touchless interactive teaching of soft robots. Reproduced with permission from ref 962. Copyright 2022 Springer Nature. \n\n8.3.1. Haptic Feedback Devices. There have been notable advancements in soft robots designed to provide sensory feedback, encompassing both cutaneous and kinesthetic domains. These devices are poised to revolutionize human−computer interaction (HCI) by offering users tactile sensations and kinesthetic information. The latest innovations in cutaneous feedback devices span a spectrum of modalities, including normal indentation, lateral stretching, vibration, and thermal feedback. \n\nThe development of the HaptGlove represents a significant breakthrough in normal indentation feedback devices for virtual reality (VR) environments. This untethered and lightweight pneumatic glove integrates haptic feedback modules and fiber sensors to provide users with immersive tactile experiences (Figure 53a).942 Users can interact with virtual objects with remarkable accuracy and dexterity, thanks to the variable stiffness force feedback and fingertip force and vibration feedback provided by the HaptGlove. Furthermore, research on modulating cutaneous force in teleoperation systems offers promising insights into enhancing haptic rendering while preserving system stability (Figure 53b).943 Moving on to lateral stretching, wearable haptic displays offer new avenues for enhancing tactile interactions in VR environments. By focusing on finger deformation and utilizing soft actuators, these devices provide users with realistic sensations of weight and inertia (Figure 53c).944 The integration of flexible hydraulically amplified electrostatic actuators in haptic sleeves demonstrates the potential for generating rich vibrotactile feedback across a wide frequency range, further enhancing the user’s sensory experience in VR and AR environments (Figure 53d).480 \n\nIn the realm of vibration feedback devices, wireless selfsensing e-skin and feel-through haptic gloves demonstrate the potential of vibration feedback devices in facilitating remote touch communication and enhancing task performance in VR and augmented reality (AR) environments (Figure 53e and Figure 53f).945,946 These innovations enable bidirectional touch interactions between users and provide rich vibrotactile feedback. The integration of extremely thin actuators in e-skin devices allows for dynamic mechanical stimulus on the skin, paving the way for realistic tactile sensations in virtual environments. Transitioning to thermal feedback, skin-like thermo-haptic devices and highly stretchable transparent heaters represent significant advancements in thermal feedback technology for VR applications (Figure 53g).947 By dynamically adjusting temperature based on user interactions, these devices enhance immersion and realism. The skin-like thermohaptic device offers both cold and hot sensation feedback, providing users with unique thermal sensations for a more immersive VR experience. Similarly, highly stretchable transparent heaters offer versatile applications in wearable electronics, including personal thermal management and healthcare purposes (Figure 53h).948 \n\nIn addition to cutaneous feedback devices, significant progress has been made in the development of kinesthetic feedback devices, particularly in the context of hand rehabilitation and VR interaction (Figure 53i).949 Soft robotic gloves and tendon-driven haptic gloves offer precise functional grasping support and realistic tactile feedback, enhancing user freedom and independence in VR environments. The portable, assistive soft robotic glove is designed to augment hand rehabilitation for individuals with grasp pathologies, providing specific bending, twisting, and extending trajectories to support the range of motion of individual fingers. On the other hand, the tendon-driven haptic glove utilizes a perception-based force distribution strategy to provide haptic feedback to users’ fingers in VR environments, enhancing the perceived realism and acuity of contact force (Figure 53j).950 Except for another two eamples for kinesthetic feedback device (Figure $53\\mathrm{k},\\mathrm{l}),{}^{951,952}$ relevant wearable devices are numerous, opening the window for next gereration of intelligent wearables for Metaverse. \n\nSignificant progress has been achieved in the evolution of soft robots designed to provide sensory feedback, encompassing both cutaneous and kinesthetic domains. These advancements hold the potential to revolutionize HCI by providing users with tactile sensations and kinesthetic information. From normal indentation to lateral stretching, vibration, and thermal feedback, recent innovations in cutaneous feedback devices have broadened the range of tactile experiences available. This collective progress represents a crucial advancement in the convergence of robotics, HCI, and immersive technologies, paving the way for a future where tactile interactions seamlessly blend the digital and physical realms. \n\n8.3.2. XR Applications and Human−Machine Interaction. In recent years, soft robotics has emerged as a transformative field with vast potential to revolutionize human−machine interaction (HMI) within extended reality (XR) environments. Soft robots, characterized by their flexibility, adaptability, and ability to mimic biological systems, offer unique advantages in creating immersive experiences and delivering realistic tactile feedback in virtual and augmented reality settings.953 \n\nSoft robotics technology has made significant strides in replicating authentic tactile sensations within virtual environments, thereby enhancing user immersion and engagement. Innovations such as haptic devices with stiffness feedback enable users to actively experience touching objects with varying degrees of hardness or softness, contributing to the creation of more realistic XR experiences (Figure 54a).954 By simulating the sense of touch, these devices bridge the gap between physical and virtual worlds, allowing users to interact with virtual objects in a more intuitive and lifelike manner. Touch feedback systems represent another promising area of soft robotics research, offering high spatial resolution and rapid refresh rates for rendering tactile stimuli in XR environments. Wearable electrotactile rendering devices, for example, provide users with the ability to perceive textures and shapes in virtual objects with unprecedented fidelity (Figure 54b).955 These systems find applications in diverse fields such as braille displays, virtual reality shopping, and digital experiences, expanding the scope of XR technology into new domains and industries. \n\nOlfaction feedback, often overlooked in traditional VR or XR systems, plays a crucial role in enhancing immersion and emotional engagement within XR environments. Skin-interfaced olfactory feedback systems leverage arrays of flexible and miniaturized odor generators to deliver programmable scentbased stimuli (Figure 54c).956 These systems find applications in entertainment, education, healthcare, and beyond, offering new avenues for sensory exploration and interaction in virtual spaces. \n\nThe integration of soft robotics with social media platforms introduces novel interactions for interactive communication and personal engagement within XR environments. Wireless platforms capable of delivering programmable mechanical vibrations enable seamless communication via the skin, facilitating social interactions, prosthetic control, and gaming experiences (Figure 54d).957 By leveraging the sense of touch as a communication channel, these systems enhance user engagement and foster meaningful connections in virtual communities. Soft robotics has also made significant contributions to gaming interfaces, with foldable origami robots offering portable solutions for haptic exploration and interaction. These robots enable users to engage with virtual environments in new and exciting ways, enhancing player immersion and interaction in gaming environment (Figure 54e).958 By providing tactile feedback and intuitive control mechanisms, these systems offer immersive gaming experiences that blur the lines between physical and virtual realities. \n\nWireless haptic interfaces have emerged as powerful tools for conveying spatial information and enhancing navigation in XR environments. These interfaces provide intuitive feedback for navigation instructions, musical translation, and sensory replacement feedback for robotic prosthetics, offering new opportunities for immersive experiences in medicine, sports, and gaming (Figure 54f).959 By leveraging the sense of touch as a means of spatial communication, these systems enable users to navigate virtual environments with greater precision and efficiency. Closed-loop human−machine interfaces based on skin-integrated electronics have transformed human−robot interaction in XR settings. These interfaces enable wireless motion capture and haptic feedback, paving the way for noncontact collection of biosamples, nursing infectious disease patients, and immersive teleoperation in healthcare application (Figure $\\displaystyle{\\langle4\\mathrm{g}\\rangle}$ .960 By integrating visual and haptic feedback, these systems provide users with a seamless and intuitive interface for interacting with robots in XR environments, enabling a wide range of applications in healthcare, entertainment, and beyond. \n\n \nFigure 55. Application of soft robot for handling, manipulation, and recognition. (a) Demonstration of the soft gripper of embedded pneumatic networks (PneuNets) within elastomers. (b) Photographs showing the demonstration of the high adaption feature of the gripper based on layer jamming. (c) Demonstration of a soft gripper based on tensile jamming. (d) Demonstration of a soft gripper based on the jamming of granular material. (e) Demonstration of a multifinger soft gripper with gecko-inspired adhesives. (f) Demonstration of the robot hand integrated with quadruple tactile sensors for garbage sorting. (g) Demonstartion of a soft prosthetic hand equipped with stretchable optical waveguides as sensors for detecting shape and texture. (h) Demonstration of an iontronic skin-based soft robotic hand for object recognition. (a) Reproduced with permission from ref 963. Copyright 2011 Wiley. (b) Reproduced with permission from ref 964. Copyright 2022 American Society of Mechanical Engineering. (c) Reproduced with permission from ref 965. Copyright 2021, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). (d) Reproduced with permission from ref 966. Copyright 2010 National Academy of Sciences. (e) Reproduced with permission from ref 967. Copyright 2021 American Association for the Advancement of Science. (f) Reproduced with permission from ref 162. Copyright 2020 American Association for the Advancement of Science. (g) Reproduced with permission from ref 118. Copyright 2016 American Association for the Advancement of Science. (h) Reproduced with permission from ref 968. Copyright 2023, The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BYNC). \n\nAdvancements in humanoid robotics have enabled robots to mimic human facial expressions in real-time, enhancing nonverbal communication and interaction within XR environments. By training robots to anticipate and coexpress facial expressions simultaneously with humans, researchers have overcome barriers to natural and genuine interaction, fostering greater social connection and empathy between humans and machines (Figure 54h).961 By enhancing the expressiveness of humanoid robots, these systems enable more engaging and intuitive interactions in XR settings, fostering greater social connection and empathy between humans and machines. Flexible sensory interfaces offer intuitive and user-friendly approaches to teaching soft robots complex movements and tasks within XR environments. Bimodal smart skin technology, for example, enables humans to teach soft robots movements via bare hand−eye coordination, empowering users to teach robots specific tasks such as completing mazes, taking throat swabs, and grasping objects (Figure 54i).962 By providing intuitive and nonprogrammable teaching methods, these interfaces democratize the use of soft robots in various applications, enabling broader adoption and utilization across diverse domains. \n\nIn conclusion, the integration of soft robots with XR systems holds immense promise for revolutionizing human−machine interaction across diverse domains. By leveraging the latest advancements in materials science, sensor technology, and human−computer interfaces, researchers continue to push the boundaries of XR and HMI, paving the way for a more connected and interactive future. As soft robotics technology continues to evolve, it is poised to play a central role in shaping the XR experiences of tomorrow, offering new avenues for creativity, exploration, and collaboration in VR and AR.",
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"chunk": "# 8.4. Manipulation \n\nSoft robotics has emerged as a transformative field, offering innovative solutions for object manipulation and recognition. Unlike traditional rigid robots, soft robots are constructed from flexible and deformable materials, allowing them to adapt to complex environments and interact with objects more naturally. These robots are inspired by biological systems, enabling them to perform delicate tasks with dexterity and precision. \n\nSoft robotic systems utilize advanced sensing technologies, such as computer vision and tactile sensors, to recognize and manipulate objects with varying shapes, sizes, and properties. By integrating soft actuators and sensors, these robots can grasp, lift, and manipulate objects with greater flexibility and sensitivity. \n\n8.4.1. Object Handling. Whitesides’s group proposed the utilization of soft materials, particularly elastomers, in crafting fully soft robots.963 Traditional rigid robots face challenges in delicately handling fragile objects and traversing unpredictable terrains. In response, soft robots offer promising solutions. The concept of soft robots encompasses machines fashioned from soft materials or those comprising multiple hard-robotic actuators operating synergistically to exhibit soft-robot-like properties. Whitesides’s group emphasizes soft elastomeric materials, highlighting their benefits, such as continuous deformation and expansive ranges of motion dictated by material properties (Figure 55a). Their approach involves designing and fabricating soft robots through embedded pneumatic networks (PneuNets) within elastomers. These networks function akin to balloons, inflating to enact movement. By employing pneumatic systems for energy supply and employing diverse materials for control, they pave the way for innovative soft robotics application. \n\nSu’s group introduces a soft robotic gripper employing layer jamming technology, offering a unique blend of high payload capacity and adaptability.964 This gripper, crafted through 3D printing with two materials, integrates jamming layers to bolster its payload capacity. Actuation occurs through inflating the internal air chamber, enabling significant bending angles. Notably, for heavy payloads, the gripper employs negative air pressure on the jamming layers, effectively locking it in the desired shape. Remarkably adaptable, it can securely grasp w80 objects ranging from 6 to $10\\mathrm{~kg},$ rivaling rigid-body grippers in performance (Figure 55b). Kramer-Bottiglio’s group reported a design termed “tensile jamming fibers,” capable of swiftly adjusting their tensile stiffness while preserving low bending stiffness (Figure 55c). These fibers find application in two key domains: modular variable trajectory actuators and shapechanging membranes. The study showcases the fibers’ prowess, presenting mechanical testing outcomes and manufacturing intricacies. The overarching aim is to realize reconfigurable soft robotics and shape-changing systems leveraging the unique attributes of tensile jamming fibers.965 Brown et al. devised a versatile gripper utilizing the principle of granular jamming.966 Instead of traditional fingers, a single mass of granular material conforms to the shape of an object upon contact, facilitated by vacuum-induced contraction and hardening (Figure 55d). Through experiments, the researchers showcased the gripper’s efficacy in grasping various objects, ranging from spheres to cubes. They also elucidated the underlying gripping mechanisms, including friction and suction, and developed a model linking these mechanisms to the jammed material’s strength. The study underscores the gripper’s potential for reliably handling objects of diverse shapes, particularly in scenarios requiring rapid and informed manipulation. Ruotolo’s group introduced a robotic hand design named farmHand, incorporating gecko-inspired adhesives for grasping and manipulation tasks (Figure 55e). The hand features compliant finger pads with angled ribs for enhanced contact and load sharing. A control strategy aligns phalange orientations to surface normals, reducing pressure inconsistencies. Testing validated load sharing and manipulation capabilities, showcasing versatility in various tasks.967 \n\n8.4.2. Object Recognition. Zhu’s group introduced a robot hand equipped with quadruple tactile sensors, enhancing object recognition during grasping .162 These sensors feature a multilayer microstructure inspired by skin, enabling perception of thermal conductivity, contact pressure, and object/environment temperature simultaneously. Through fusion with machine learning, the hand can accurately identify diverse objects based on shape, size, and material (Figure 55f). The researchers demonstrated its efficacy in garbage sorting, achieving a remarkable $94\\%$ classification accuracy across seven types of garbage. Shepherd’s group introduced a soft prosthetic hand featuring stretchable optical waveguides serving as sensors for strain, curvature, elongation, and force detection.118 These waveguides are crafted via a four-step soft lithography procedure, comprising a core material with a high refractive index and a cladding material with a low refractive index. With exceptional compliance and stretchability, these waveguides function effectively as sensors for diverse deformations. Integrated into the fingers of the prosthetic hand, they facilitate active sensation experiments, including shape and softness detection (Figure 55g). Guo’s group devised a highly sensitive and mechanically robust iontronic skin for robotics, embedding isolated microstructured ionic gels (IMIGs) within an elastomeric matrix.968 This design ensured heightened sensitivity, minimal response to shear stress, and swift response-recovery rates. The skin exhibited resilience under severe mechanical conditions, enabling realtime pressure mapping and object recognition (Figure 55h).",
|
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"category": " Results and discussion"
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},
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{
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"id": 50,
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"chunk": "# 9. CONSIDERATIONS FOR FUTURE DEVELOPMENT \n\nUndoubtably, numerous advancements have been made in the field of soft robots in terms of sensing, actuation, control, and applications. These advancements have not only changed the research paradigm but also the way human beings interact with the world. However, as we look toward the future development of sensorimotor materials and soft robots, several key considerations should be taken into account.117,559 As summarized in Figure 56, we categorize these considerations into materials discovery, biomimicking, energy, manufacturing, artificial intelligence, and sustainability. \n\n \nFigure 56. Summary of future development considerations of soft intelligent machines.",
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||
"category": " Conclusions"
|
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},
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{
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"id": 51,
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"chunk": "# 9.1. Materials Discovery \n\nThe discovery of new materials will undoubtfully drive the development of soft robotics and flexible electronics, offering significant advantages in terms of functionality, efficiency, and adaptability.969,970 For example, the debut of stimuli responsive smart materials (LCEs, magnetic fluids, shape-memory polymers, etc.) opens new avenues for the development of untethered soft robots,971,972 and the introduction of conductive polymers addresses the dilemma between mechanical flexibility and electrical conductivity of materials,973 enabling the integration of electronic devices in an unprecedented way. Despite numerous breakthroughs in materials science, such as graphene, conductive polymers, 2D materials, hydrogels, and ion gels, the search of novel materials with better performances and functionalities is endless and there’s plenty of space within this discovery. For example, biohybrid materials that combines synthetic materials with biological components (like cells or proteins) allows for the creation of living, responsive systems for applications in medical robotics and tissue engineering.974−978 On the other hand, metamaterials, engineered with properties not found in natural materials, are also revolutionizing electronics and robotics. Such materials could not only enable the miniaturization of high-performance antennas and improve electromagnetic wave manipulation in electronics, but also can empower soft robots with unconventional functionalities ranging from actuation and computation to signal processin g.979,980 Another interesting point is edible robotic food that can serve as functional components, such as health monitoring and drug delivery, which could open a new avenue for robotics, healthcare, and the environment.981 As research progresses, these new materials enable the development of more advanced and durable systems that can operate in a variety of environments and applications, from medical devices to wearable technology and beyond. Their unique properties allow for the creation of robots and electronics that are lighter, more energy-efficient, and capable of performing complex tasks, ultimately expanding the potential applications of these technologies.",
|
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"category": " Introduction"
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},
|
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{
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"id": 52,
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"chunk": "# 9.2. Biomimicking \n\nBiomimicking, or biomimicry, leverages nature’s evolved solutions to create innovative and efficient technologies across various fields. By emulating natural designs and processes honed over millions of years, biomimicking leads to advancements in both flexible electronic devices and soft robots.982−984 For example, octopus-inspired soft robots offer superior adaptability and delicate interaction capabilities,137,985 while gecko-inspired adhesives enhance wearable electronic devices’ comfort and effectiveness. 6 Such biomimicking approach also fosters eco-friendly and energy-efficient solutions, such as selfcleaning surfaces for efficient photovoltaics and chameleoninspired color-changing materials for flexible displays.987,988 Moreover, biomimicking also drives medical advancements, improving prosthetics’ designs and functionalities. Despite these advancements, current technologies have achieved biomimicry at a relatively low level, which only exhibits limited functionality, lacking the intricacy of multiple functionalities and subsystems as well as their coordination in an efficient and reliable way. By contrast, biological species exhibit complex, integrated sensorimotor systems that enable highly responsive, adaptive, and efficient interactions with their environment. Thus, future research should delve deeper into these mechanisms, studying the intricate neural networks, sensory feedback loops, and adaptive learning processes that animals use. Taking the research of e-skin as an example, rather than focusing only on the detection of pressure, it is crucial to achieve other functions possessed by real skin. Real skin not only senses pressure but also has the ability to detect temperature, differentiate between normal and shear forces, and respond to both static and dynamic stimuli.989,990 Moreover, it has self-healing properties that enable it to repair damage and maintain functionality over time.991 As for the conventional signal processing approaches involved in e-skin, while the data acquisition is based on sequential measurement of time division multiple access at each measurement cycle, the information processing mainly utilizes analytical or data-driven approaches.992,993 Unavoidably, a number of challenges are involved in such approaches, ranging from complex switching circuits and readout latency to signal interference and power consumption.994 On the contrary, biological species employs event-driven spike generation solution that allows parallel data processing without the readout delay, which can greatly reduce the power consumption to process volumes of sensing dat a.995,996 Therefore, such signal processing techniques as well as neuromorphic computing in biological species should be further deployed in the development of intelligent soft machines. $430,997,998$ Moving further to sensorimotor control, it has been found that hierarchical control strategies are widely used in animals for the intricate movement of muscles.4,999 These strategies involve multiple layers of control, from highlevel planning and coordination to low-level execution of precise movements, allowing animals to perform complex tasks efficiently and adaptively. By studying these natural hierarchical control systems, we can develop novel and powerful control technologies for soft robots.1000−1007 In a word, we can develop more advanced, autonomous, and intelligent materials, robots, and systems by closely replicating these sophisticated mechanisms in biological systems.",
|
||
"category": " Introduction"
|
||
},
|
||
{
|
||
"id": 53,
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"chunk": "# 9.3. Energy \n\nThe significance of energy in soft robots and electronic devices cannot be overstated. As these technologies continue to advance, it poses significant challenges for the development of next-generation power sources.1008−1010 These challenges include balancing size, weight, flexibility and other physical properties with power functions. As smaller devices and robots face considerable payload restrictions and energy requirements, careful consideration of the size and weight of energy sources during the design phase is crucial.1011 The compactness and lightweight nature of these devices necessitate efficient energy storage solutions that do not compromise their operational capabilities. Balancing energy density with size, weight, and structures constraints is essential to ensure that these devices can perform effectively without frequent recharging or added bulk.1012 Additionally, the development of soft, flexible, and stretchable batteries could greatly contribute to the advancement of all soft electronic devices and systems.1013 Incorporating such energy storage solutions allows for greater design freedom, enabling the creation of more ergonomic, compact and user-friendly devices that can seamlessly integrate with the human body or adapt to dynamic environments. Apart from these examples, there are many other application scenarios that require the next-generation energy sources. While maintaining efficient energy storage and management in compact and flexible forms is difficult, advancements in materials, nanotechnology, and energy harvesting from ambient environment offer promising solutions.1014−1017",
|
||
"category": " Results and discussion"
|
||
},
|
||
{
|
||
"id": 54,
|
||
"chunk": "# 9.4. Manufacturing \n\nThe goal of advanced manufacturing in the future is to create highly adaptable, efficient, and high-performing systems that outperform their predecessor and meet diverse application needs. The birth of such systems will be driven by innovations and technological breakthroughs such as 4D printing, multimaterial printing, seamless integration technology, nanomanufacturing, digital twins, AI, and sustainable practices. Different from 3D printing, 4D printing will create dynamic, responsive structures that adapt to environmental stimuli, while multimaterial and hybrid manufacturing will seamlessly combine diverse materials for enhanced functionality.1018−1020 Nanomanufacturing will enable precise nanoscale features and utilize nanomaterials to boost performance. Digital twins and simulations will optimize design and maintenance, reducing development time and costs.1021 AI integration will enhance automation, precision, and efficiency in manufacturing processes. Sustainable manufacturing will focus on biocompatible, eco-friendly materials and energy-efficient processes, reducing environmental impact. By adopting and coordinating these cutting-edge techniques, the future of these fields promises improved solutions, greater sustainability, and innovative applications across diverse industries.",
|
||
"category": " Introduction"
|
||
},
|
||
{
|
||
"id": 55,
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||
"chunk": "# 9.5. Artificial Intelligence \n\nIt can be envisioned that AI will be indispensable in soft robotics in near future, playing a crucial role in various aspects from design optimization and manufacturing process management to signal processing, analysis, decision-making and control. AI-driven algorithms will optimize the design of soft robots, ensuring their efficiency, performance, and adaptability to specific tasks and environments. In manufacturing, AI will manage processes, reducing waste, enhancing quality control, and improving efficiency through predictive maintenance and real-time monitoring.1022 More interestingly, AI will be part of soft robots itself, acting as the minds and enabling them to process and analyze sensory data, making informed decisions and adjustments for control in real-time .1023 With the assist from AI, the autonomy, functionality, and intelligence of soft robots can be enhanced in an unprecedented level.1024 On the other hand, the integration of large models (such as generative pretrained transformer, GPT) into soft robotics holds significant potential for enhancing human−robot interaction, programming and control, problem-solving and optimization, training and learning, and assistive application s.1025−1031 These AI models excel in natural language processing, enabling users to interact with soft robots using spoken or written language, simplifying programming tasks, and facilitating more intuitive communication. However, challenges such as ensuring robustness, accuracy, safety, and addressing ethical considerations need to be addressed to ensure responsible deployment and adoption.",
|
||
"category": " Introduction"
|
||
},
|
||
{
|
||
"id": 56,
|
||
"chunk": "# 9.6. Sustainability \n\nAiming at mitigating environmental impacts, promoting resource conservation, and fostering long-term viability in the field of technology, sustainability entails a comprehensive approach focusing on materials innovation, green manufacturing, energy efficiency, circular design principles, regulatory compliance, and public engagement.1032 To be more specific, this includes the adoption of biodegradable and recyclable materials, implementation of sustainable manufacturing practices, improvement of energy efficiency through energy harvesting and storage, and design for disassembly and upgradability to extend lifespans and reduce wastes from both electronic devices and soft robots.751,1033−1037 Lifecycle assessments and adherence to environmental regulations will be paramount, along with efforts to increase public awareness and education about sustainable practices. By prioritizing sustainability throughout the product lifecycle, manufacturers can create more environmentally friendly and socially responsible technologies, contributing to a greener and more sustainable future. Ultimately, the significance of advancing sustainability in soft robots and flexible electronic devices extends beyond technological innovation to encompass broader societal and environmental benefits, contributing to a more sustainable future for generations to come.",
|
||
"category": " Introduction"
|
||
},
|
||
{
|
||
"id": 57,
|
||
"chunk": "# 10. CONCLUDING REMARKS \n\nThe intersection between flexible sensing devices and soft robots is an emerging field poised for the continuing development of soft intelligent machines and their real-world applications for exploration, healthcare, entertainment, and industry by providing enhanced adaptability, safer interactions, and the ability to function in unstructured environments. Despite numerous advancements being made in soft materials and actuators, multifunctional flexible sensors, advanced control systems, and energy-efficient power supplies over the years, the marriage between soft electronic device and robot is just in its early stage and corresponding challenges do exist. These include devising the output performance of soft sensors and actuators according to needs, ensuring the durability and reliability of flexible components under repetitive stress and harsh conditions, achieving seamless integration between soft materials and electronic devices, improving the robustness of the sensorimotor control system, and developing scalable and cost-effective manufacturing processes. \n\nTraditionally, sensors and actuators in soft robotics have been developed and optimized independently, which can result in suboptimal performance, energy inefficiencies, and a lack of seamless interaction between components. By adopting a codesign approach, the sensor and actuator systems are developed concurrently from systematic design perspective, with the design of each component informed by the requirements and constraints of the other. This integrated approach enables more efficient, adaptive, and responsive systems, where the actuation can be directly informed by sensory feedback, and the sensor can be optimized to capture the most relevant physical parameters that enhance actuator performance. Co-optimization takes this integration a step further, focusing on the simultaneous optimization of both components to maximize system performance. By considering the sensor and actuator as a unified system, co-optimization ensures that the functionalities of each component complement each other, leading to improved energy efficiency, faster response times, and more precise control. For example, soft actuators can be designed to deform in a way that generates specific sensory feedback, allowing for continuous, real-time adjustments to the system’s behavior. Similarly, sensors can be optimized to detect parameters that are crucial for actuator control, such as strain, pressure, and temperature, enabling the actuation system to adapt dynamically to environmental conditions and internal changes. \n\nSuch codesign and co-optimization of soft sensors and actuators are also crucial for developing adaptive soft robotic systems with sensorimotor functions. By optimizing relevant components in tandem, closed-loop, energy-efficient, adaptive, and intelligent systems can be created, where real-time sensor feedback informs and adjusts actuator behavior in unstructured environments. This is particularly valuable in complex tasks such as manipulation, where precise coordination between the sensor and actuator is necessary for tasks such as object grasping or delicate assembly. The integration of these elements not only enhances the functionality of soft robots but also contributes to their robustness, adaptability and autonomy in real-world applications. Moreover, advances in materials science play a pivotal role in the codesign and cooptimization process. The development of materials that simultaneously enable both sensing and actuation functions is key to simplifying the design and reducing the weight and complexity of soft robotic systems. Materials such as piezoelectric polymers, electroactive elastomers, or multifunctional composites can serve both as sensors and actuators, offering significant advantages in terms of compactness, efficiency, and system integration. The selection of such materials allows for the development of highly integrated systems that reduce the need for external wiring and support structures, thereby enhancing the system’s flexibility and scalability. In a word, the codesign and co-optimization of soft sensors and actuators represent an essential direction for future research in soft robotics. This integrated approach has the potential to significantly enhance the intelligence, adaptability, and efficiency of soft robots, paving the way for their application in complex and dynamic environments such as healthcare, human−robot interaction, and autonomous systems. Future work should focus on refining computational models and control strategies that facilitate the optimization of sensor-actuator systems, as well as exploring new materials that support multifunctionality. Ultimately, the integration of sensing and actuation in a cohesive, optimized system will be key to unlocking the full potential of soft robots. \n\nThe ultimate goal of soft robotic research is to develop robots that possess intelligence comparable to or even beyond that of humans, enabling them to naturally interact with their environment, learn from experiences, and autonomously perform complex tasks. Achieving this vision, undoubtedly, requires a multidisciplinary approach that draws upon the expertise of biologists, neuroscientists, materials scientists, roboticists, and engineers. Each discipline contributes valuable insights and techniques, such as biological inspiration for sensorimotor integration, advances in materials for flexible actuation, and breakthroughs in AI for autonomous decisionmaking. The convergence of these fields will facilitate the creation of highly efficient and adaptable sensorimotor systems, which can respond intelligently to dynamic environments and complex scenarios. The future of soft robots holds immense promise, and with continued collaboration, innovation, and dedication, we are on the cusp of realizing a new era of autonomy, intelligence, and sustainability.",
|
||
"category": " Conclusions"
|
||
},
|
||
{
|
||
"id": 58,
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||
"chunk": "# AUTHOR INFORMATION",
|
||
"category": " References"
|
||
},
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||
{
|
||
"id": 59,
|
||
"chunk": "# Corresponding Author \n\nXiaodong Chen − Innovative Centre for Flexible Devices (iFLEX), Max Planck−NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore; $\\circledcirc$ orcid.org/0000-0002-3312-1664; Email: chenxd $@$ ntu.edu.sg",
|
||
"category": " References"
|
||
},
|
||
{
|
||
"id": 60,
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||
"chunk": "# Authors \n\nJiangtao Su − Innovative Centre for Flexible Devices (iFLEX), Max Planck−NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore Ke He − Innovative Centre for Flexible Devices (iFLEX), Max Planck−NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore Yanzhen Li − Innovative Centre for Flexible Devices (iFLEX), Max Planck−NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore Jiaqi Tu − Innovative Centre for Flexible Devices (iFLEX), Max Planck−NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore",
|
||
"category": " References"
|
||
},
|
||
{
|
||
"id": 61,
|
||
"chunk": "# Author Contributions \n\nX.C. and J.S. conceived the topic and proposed the structure of this review. J.S., K.H., Y.L., and J.T. carried out data curation and analysis, produced the figures, and prepared the manuscript in consultation with X.C. CRediT: Jiangtao Su conceptualization, data curation, formal analysis, visualization, writing - original draft, writing - review & editing; Ke He conceptualization, formal analysis, visualization, writing - original draft; Yanzhen Li conceptualization, data curation, writing - original draft; Jiaqi Tu data curation, formal analysis, writing - original draft; Xiaodong Chen conceptualization, formal analysis, investigation, project administration, resources, supervision, visualization, writing - original draft, writing - review $\\&$ editing.",
|
||
"category": " Abstract"
|
||
},
|
||
{
|
||
"id": 62,
|
||
"chunk": "# Notes \n\nThe authors declare no competing financial interest.",
|
||
"category": " References"
|
||
},
|
||
{
|
||
"id": 63,
|
||
"chunk": "# Biographies \n\nJiangtao Su is a Ph.D. candidate at the School of Materials Science and Engineering, Nanyang Technological University in Singapore, and he is also an academic visiting guest at The Institute of Robotics and Intelligent Systems, Department of Mechanical & Process Engineering, ETH Zurich in Switzerland. His research focuses on flexible devices, robotics, metamaterials and advanced manufacturing. Ke He is a dedicated research fellow at the School of Materials Science and Engineering, Nanyang Technological University in Singapore. He received his Ph.D. degree in Physical Chemistry from Jilin University in 2014. His research interests primarily revolve around bioinspired materials and devices, with a specific focus on biomimicking, soft robotics, and wearable healthcare applications. \n\nYanzhen Li is pursuing his Ph.D. in the School of Materials Science and Engineering at Nanyang Technological University, Singapore. He received his B.Sc. in Materials Science of Physics from Nanjing University in 2020. His research focuses on mechanical sensors and other flexible electronic devices. \n\nTu Jiaqi is currently pursuing his Ph.D. at the School of Materials Science and Engineering, Nanyang Technological University in Singapore. He received his Bachelor’s degree in Materials Science and Engineering from Zhejiang University. His research interests lie in the development and application of flexible tactile sensors, with a particular focus on enhancing sensor performance and reliability for various applications, including wearable technology and healthcare monitoring. \n\nXiaodong Chen is a Distinguished University Professor at Nanyang Technological University, Singapore (NTU), holding appointments as Professor of Materials Science and Engineering, and Professor (by courtesy) of Chemistry and Medicine. A leading expert in the areas of flexible materials and devices, nanobio interface, and nanoelectronics, he currently serves as Editor-in-Chief of ACS Nano. He is a Fellow of Singapore National Academy of Science, a Fellow of the Academy of Engineering Singapore, and a member of the German National Academy of Sciences Leopoldina. His contributions to science and engineering have been recognized with numerous prestigious awards and honors, including Singapore President’s Science Award, Singapore NRF Investigatorship, Friedrich Wilhelm Bessel Research Award, the Dan Maydan Prize in Nanoscience and Nanotechnology, and the Kabiller Young Investigator Award.",
|
||
"category": " References"
|
||
},
|
||
{
|
||
"id": 64,
|
||
"chunk": "# ACKNOWLEDGMENTS \n\nFinancial support was provided by the Agency for Science, Technology and Research (A\\*STAR) under its AME Programmatic Funding Scheme (Project #A18A1b0045), the National Research Foundation, Singapore (NRF) under NRF’s Medium Sized Centre: Singapore Hybrid-Integrated NextGeneration $\\mu$ -Electronics (SHINE) Centre funding programme and the Smart Grippers for Soft Robotics (SGSR) Programme under the National Research Foundation, Prime Minister’s Office, Singapore under its Campus of Research Excellence and Technological Enterprise (CREATE) programme.",
|
||
"category": " References"
|
||
},
|
||
{
|
||
"id": 65,
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||
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