Ultra-sensitive and resilient compliant strain gauges for soft machines

Multi-scale microstructural construction in ultralight graphene aerogels enables super elasticity and unprecedented durability for impact protection materials

Ultralight graphene aerogels have gained extensive recognition in the impact protection field. However, attaining both elasticity and durability at low material density is challenging due to their intrinsic conflicts. Inspired by the mantis ootheca, we present a simultaneous improvement in the elasticity, durability, and density restrictions of ultralight graphene aerogels via constructing a multiscale honeycomb microstructure (MHM) within the graphene skeleton. This approach enables resulting graphene aerogel to achieve a strength per unit volume of 284.6 cm3 mg-1, the ability to recover its shape within 10 ms after an impact at 3.569 m/s, and maintain 97.2 % of its sample height after 20,000 cycles at 90 % strain. The operand analyses and calculation results reveal that the MHM structure facilitates this aerogel's dual-stage stress transfer pathway. Initially, the macroscale honeycomb structure (millimeter-scale) of the graphene aerogels bear and transmit stress to the surrounding regions, followed by the microscale honeycomb structure (micron-scale) deformation to convert stress kinetic energy into elastic potential energy. This two-stage stress transition mechanism of the MHM structure can effectively mitigate excessive local stress and suppress strain localization, thus providing remarkable elasticity and durability. Ultimately, the obtained graphene aerogel demonstrates promising applications as a fall height detection device and impact protective material.

Encapsulated stretchable amphibious strain sensors

Soft and stretchable strain sensors have found wide applications in health monitoring, motion tracking, and robotic sensing. There is a growing demand for strain sensors in amphibious environments, such as implantable sensors, wearable sensors for swimmers/divers, and underwater robotic sensors. However, developing a sensitive, stretchable, and robust amphibious strain sensor remains challenging. This work presents an encapsulated stretchable amphibious strain sensor. The conductive layer, made of silver nanowires embedded below the surface of polydimethylsiloxane, was sandwiched by two layers of thermoplastic polyurethane. Periodic sharp cuts were introduced to change the direction of flow from across the sensor to along the conductive path defined by the opening cracks. The crack advancing and opening is controlled by a unique combination of weak/strong interfaces within the sandwich structure. The cut design and the interfacial interactions between the layers were investigated. The strain sensor exhibited a high gauge factor up to 289, a linear sensing response, a fast response time (53 ms), excellent robustness against over-strain, and stability after 16 000 loading cycles and 20 days in an aqueous saline solution. The functionality of this amphibious strain sensor was demonstrated by tracking the motion of a robotic fish, undertaking language recognition underwater, and monitoring the blood pressure of a porcine aorta. This illustrates the promising potential for this strain sensor for both underwater use and surgically implantable applications.

Breaking the Saturation of Sensitivity for Ultrawide Range Flexible Pressure Sensors by Soft-Strain Effect

The flexible pressure sensors with a broad pressure range and unsaturated sensitivity are highly desired in practical applications. However, pressure sensors by piezoresistive effect are always limited by the compressibility of sensing layers, resulting in a theoretically decreasing sensitivity of less than 100%. Here, a unique strategy is proposed that utilizes the strain effect, simultaneously achieving a trade-off between a wider pressure detection range and unsaturated sensitivity. Ascribed to the strain effect of sensing layers induced by interlaced microdomes, the sensors possess an increased sensitivity (5.22-70 MPa-1) over an ultrawide pressure range (45 Pa-4.1 MPa), a high-pressure resolution (5 Pa), fast response/recovery time (30/45 ms), and a robust response under a high-pressure loading of 3.5 MPa for more than 5000 cycles. These superior sensing performances allow the sensor to monitor large pressure. The flexible pressure sensor array can assist doctors in restoring the neutral mechanical axis, tracking knee flexion angles, and extracting gait features. Moreover, the flexible sensing array can be integrated into the joint motion surveillance system to map the balance medial-lateral contact forces on the metal compartments in real time, demonstrating the potential for further development into precise medical human-machine interfaces during total knee replacement surgery.

Data-driven inverse design of flexible pressure sensors

Artificial skins or flexible pressure sensors that mimic human cutaneous mechanoreceptors transduce tactile stimuli to quantitative electrical signals. Conventional trial-and-error designs for such devices follow a forward structure-to-property routine, which is usually time-consuming and determines one possible solution in one run. Data-driven inverse design can precisely target desired functions while showing far higher productivity, however, it is still absent for flexible pressure sensors because of the difficulties in acquiring a large amount of data. Here, we report a property-to-structure inverse design of flexible pressure sensors, exhibiting a significantly greater efficiency than the conventional routine. We use a reduced-order model that analytically constrains the design scope and an iterative "jumping-selection" method together with a surrogate model that enhances data screening. As an exemplary scenario, hundreds of solutions that overcome the intrinsic signal saturation have been predicted by the inverse method, validating for a variety of material systems. The success in property design on multiple indicators demonstrates that the proposed inverse design is an efficient and powerful tool to target multifarious applications of flexible pressure sensors, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces.

Multilayer Core-Shell Fiber Device for Improved Strain Sensing and Supercapacitor Applications

1D fiber devices, known for their exceptional flexibility and seamless integration capabilities, often face trade-offs between desired wearable application characteristics and actual performance. In this study, a multilayer device composed of carbon nanotube (CNT), transition metal carbides/nitrides (MXenes), and cotton fibers, fabricated using a dry spinning method is presented, which significantly enhances both strain sensing and supercapacitor functionality. This core-shell fiber design achieves a record-high sensitivity (GF ≈ 4500) and maintains robust durability under various environmental conditions. Furthermore, the design approach markedly influences capacitance, correlating with the percentage of active material used. Through systematic optimization, the fiber device exhibited a capacitance 26-fold greater than that of a standard neat CNT fiber, emphasizing the crucial role of innovative design and high active material loading in improving device performance.

Stretchable and Self-Powered Mechanoluminescent Triboelectric Nanogenerator Fibers toward Wearable Amphibious Electro-Optical Sensor Textiles

Flexible electro-optical dual-mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital-visual signals show good prospects in smart human-machine interaction interfaces. However, heavy mass, low stretchability, and lack of non-contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self-powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent-triboelectric synergistic effect, the well-designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non-contact sensing capability with a detection distance of up to 35 cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro-optical textile electronics for smart human-machine interaction interfaces.

Human Activity Recording Based on Skin-Strain-Actuated Microfluidic Pumping in Asymmetrically Designed Micro-Channels

The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the recording of human activity in the fluidic domain. An analytical model describing the SAMP's operation mechanism as a wearable microfluidic device was established. Fabrication of the SAMP was achieved using soft lithography from polydimethylsiloxane (PDMS). Benchtop experimental results and theoretical predictions were shown to be in good agreement. The SAMP was mounted on human skin and experiments conducted on volunteer subjects demonstrated the SAMP's capability to record human activity for hundreds of cycles in the fluidic domain through the observation of a stable liquid meniscus. Proof-of-concept experiments further revealed that the SAMP could quantify a single wrist activity repetition or distinguish between three different shoulder activities.

Review of Recent Progress on Silicone Rubber Composites for Multifunctional Sensor Systems

The latest progress (the year 2021-2024) on multifunctional sensors based on silicone rubber is reported. These multifunctional sensors are useful for real-time monitoring through relative resistance, relative current change, and relative capacitance types. The present review contains a brief overview and literature survey on the sensors and their multifunctionalities. This contains an introduction to the different functionalities of these sensors. Following the introduction, the survey on the types of filler or rubber and their fabrication are briefly described. The coming section deals with the fabrication methodology of these composites where the sensors are integrated. The special focus on mechanical and electro-mechanical properties is discussed. Electro-mechanical properties with a special focus on response time, linearity, and gauge factor are reported. The next section of this review reports the filler dispersion and its role in influencing the properties and applications of these sensors. Finally, various types of sensors are briefly reported. These sensors are useful for monitoring human body motions, breathing activity, environment or breathing humidity, organic gas sensing, and, finally, smart textiles. Ultimately, the study summarizes the key takeaway from this review article. These conclusions are focused on the merits and demerits of the sensors and are followed by their future prospects.

Ag-thiolate interactions to enable an ultrasensitive and stretchable MXene strain sensor with high temporospatial resolution

High-sensitivity strain sensing elements with a wide strain range, fast response, high stability, and small sensing areas are desirable for constructing strain sensor arrays with high temporospatial resolution. However, current strain sensors rely on crack-based conductive materials having an inherent tradeoff between their sensing area and performance. Here, we present a molecular-level crack modulation strategy in which we use layer-by-layer assembly to introduce strong, dynamic, and reversible coordination bonds in an MXene and silver nanowire-matrixed conductive film. We use this approach to fabricate a crack-based stretchable strain sensor with a very small sensing area (0.25 mm2). It also exhibits an ultrawide working strain range (0.001-37%), high sensitivity (gauge factor ~500 at 0.001% and >150,000 at 35%), fast response time, low hysteresis, and excellent long-term stability. Based on this high-performance sensing element and facile assembly process, a stretchable strain sensor array with a device density of 100 sensors per cm2 is realized. We demonstrate the practical use of the high-density strain sensor array as a multichannel pulse sensing system for monitoring pulses in terms of their spatiotemporal resolution.

Organic-inorganic hybrid piezotronic bipolar junction transistor for pressure sensing

With the rapid development of the Internet of Things (IoTs), wearable sensors are playing an increasingly important role in daily monitoring of personal health and wellness. The signal-to-noise-ratio has become the most critical performance factor to consider. To enhance it, on the one hand, good sensing materials/devices have been employed; on the other hand, signal amplification and noise reduction circuits have been used. However, most of these devices and circuits work in an active sampling mode, requiring frequent data acquisition and hence, entailing high-power consumption. In this scenario, a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great interest. Here, we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor (PBJT) that acts as an autonomous and highly sensitive, current- and/or voltage-mediated pressure sensor. The PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collector-base diodes. Upon force exertion on the emitter side, as a result of the piezoelectric effect, the emitter-base diode is forward biased while the collector-base diode is reverse biased. Due to the inherent BJT amplification effect, the PBJT achieves record-high sensitivities of 139.7 kPa-1 (current-based) and 88.66 kPa-1 (voltage-based) in sensing mode. The PBJT also has a fast response time of <110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger touch. Therefore, the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous, making it promising for pressure sensing and other IoT applications.

Microstrain-Stimulated Elastico-Mechanoluminescence with Dual-Mode Stress Sensing

Elastico-mechanoluminescence technology has shown significant application prospects in stress sensing, artificial skin, remote interaction, and other research areas. Its progress mainly lies in realizing stress visualization and 2D or even 3D stress-sensing effects using a passive sensing mode. However, the widespread promotion of mechanoluminescence (ML) technology is hindered by issues such as high stress or strain thresholds and a single sensing mode based on luminous intensity. In this study, a highly efficient green-emitting ML with dual-mode stress-sensing characteristics driven by microscale strain is developed using LiTaO3:Tb3+. In addition to single-mode sensing based on the luminous intensity, the self-defined parameter (Q) is also introduced as a dual-mode factor for sensing the stress velocity. Impressively, the fabricated LiTaO3:Tb3+ film is capable of generating discernible ML signals even when supplied with strains as low as 500 µst. This is the current minimum strain value that can drive green-emitting ML. This study offers an ideal photonic platform for exploring the potential applications of rare-earth-doped elastico-ML materials in remote interaction devices, high-precision stress sensors, and single-molecule biological imaging.

Piezotronic Transistors Based on GaN Wafer for Highly Sensitive Pressure Sensing with High Linearity and High Stability

Piezotronic effect utilizing strain-induced piezoelectric polarization to achieve interfacial engineering in semiconductor nanodevices exhibits great advantages in applications such as human-machine interfacing, micro/nanoelectromechanical systems, and next-generation sensors and transducers. However, it is a big challenge but highly desired to develop a highly sensitive piezotronic device based on piezoelectric semiconductor wafers and thus to push piezotronics toward wafer-scale applications. Here, we develop a bicrystal barrier-based piezotronic transistor for highly sensitive pressure sensing by p-GaN single-crystal wafers. Its pressure sensitivity can be as high as 19.83 meV/MPa, which is more than 15 times higher than previous bulk-material-based piezotronic transistors and reaches the level of nanomaterial-based piezotronic transistors. Moreover, it can respond to a very small strain of 3.3 × 10-6 to 1.1 × 10-5 with high gauge factors of 1.45 × 105 to 1.38 × 106, which is a very high value among various strain sensors. Additionally, it also exhibits high stability (current stability of 97.32 ± 2.05% and barrier height change stability of 95.85 ± 3.43%) and high linearity (R2 ∼ 0.997 ± 0.002) in pressure sensing. This work proves the possibility of designing a bicrystal barrier as the interface to obtain a strong piezotronic effect and highly sensitive piezotronic devices based on wafers, which contributes to their applications.

Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.

High-density, highly sensitive sensor array of spiky carbon nanospheres for strain field mapping

While accurate mapping of strain distribution is crucial for assessing stress concentration and estimating fatigue life in engineering applications, conventional strain sensor arrays face a great challenge in balancing sensitivity and sensing density for effective strain mapping. In this study, we present a Fowler-Nordheim tunneling effect of monodispersed spiky carbon nanosphere array on polydimethylsiloxane as strain sensor arrays to achieve a sensitivity up to 70,000, a sensing density of 100 pixel cm-2, and logarithmic linearity over 99% within a wide strain range of 0% to 60%. The highly ordered assembly of spiky carbon nanospheres in each unit also ensures high inter-unit consistency (standard deviation ≤3.82%). Furthermore, this sensor array can conformally cover diverse surfaces, enabling accurate acquisition of strain distributions. The sensing array offers a convenient approach for mapping strain fields in various applications such as flexible electronics, soft robotics, biomechanics, and structure health monitoring.

Self-Healing Hydrogel Bioelectronics

Hydrogels have emerged as powerful building blocks to develop various soft bioelectronics because of their tissue-like mechanical properties, superior bio-compatibility, the ability to conduct both electrons and ions, and multiple stimuli-responsiveness. However, hydrogels are vulnerable to mechanical damage, which limits their usage in developing durable hydrogel-based bioelectronics. Self-healing hydrogels aim to endow bioelectronics with the property of repairing specific functions after mechanical failure, thus improving their durability, reliability, and longevity. This review discusses recent advances in self-healing hydrogels, from the self-healing mechanisms, material chemistry, and strategies for multiple properties improvement of hydrogel materials, to the design, fabrication, and applications of various hydrogel-based bioelectronics, including wearable physical and biochemical sensors, supercapacitors, flexible display devices, triboelectric nanogenerators (TENGs), implantable bioelectronics, etc. Furthermore, the persisting challenges hampering the development of self-healing hydrogel bioelectronics and their prospects are proposed. This review is expected to expedite the research and applications of self-healing hydrogels for various self-healing bioelectronics.

Somatosensory Electro-Thermal Actuator through the Laser-Induced Graphene Technology

The biological system realizes the unity of action and perception through the muscle tissue and nervous system. Correspondingly, artificial soft actuators realize the unity of sensing and actuating functions in a single functional material, which will have tremendous potential for developing intelligent and bionic soft robotics. This paper reports the design of a laser-induced graphene (LIG) electrothermal actuator with self-sensing capability. LIG, a functional material formed by a one-step direct-write lasing procedure under ambient air, is used as electrothermal conversion materials and piezoresistive sensing materials. By transferring LIG to a flexible silicone substrate, the design ability of the LIG-based actuator unit is enriched, along with an effectively improved sensing sensitivity. Through the integration of different types of well-designed LIG-based actuator units, the transformations from multidimensional precursors to 2D and 3D structures are realized. According to the piezoresistive effect of the LIG units during the deformation process, the visual synchronous deformation state feedback of the LIG-based actuator is proposed. The multimodal crawling soft robotics and the switchable electromagnetic shielding cloak serve as the demonstrations of the self-sensing LIG-based actuator, showing the advantage of the design in remote control of the soft robot without relying on the assistance of visual devices.

Flexible sensors with zero Poisson's ratio

Flexible sensors have been developed for the perception of various stimuli. However, complex deformation, usually resulting from forces or strains from multi-axes, can be challenging to measure due to the lack of independent perception of multiaxial stimuli. Herein, flexible sensors based on the metamaterial membrane with zero Poisson's ratio (ZPR) are proposed to achieve independent detection of biaxial stimuli. By deliberately designing the geometric dimensions and arrangement parameters of elements, the Poisson's ratio of an elastomer membrane can be modulated from negative to positive, and the ZPR membrane can maintain a constant transverse dimension under longitudinal stimuli. Due to the accurate monitoring of grasping force by ZPR sensors that are insensitive to curvatures of contact surfaces, rigid robotic manipulators can be guided to safely grasp deformable objects. Meanwhile, the ZPR sensor can also precisely distinguish different states of manipulators. When ZPR sensors are attached to a thermal-actuation soft robot, they can accurately detect the moving distance and direction. This work presents a new strategy for independent biaxial stimuli perception through the design of mechanical metamaterials, and may inspire the future development of advanced flexible sensors for healthcare, human-machine interfaces and robotic tactile sensing.

Soft mechanical sensors for wearable and implantable applications

Wearable and implantable sensing of biomechanical signals such as pressure, strain, shear, and vibration can enable a multitude of human-integrated applications, including on-skin monitoring of vital signs, motion tracking, monitoring of internal organ condition, restoration of lost/impaired mechanoreception, among many others. The mechanical conformability of such sensors to the human skin and tissue is critical to enhancing their biocompatibility and sensing accuracy. As such, in the recent decade, significant efforts have been made in the development of soft mechanical sensors. To satisfy the requirements of different wearable and implantable applications, such sensors have been imparted with various additional properties to make them better suited for the varied contexts of human-integrated applications. In this review, focusing on the four major types of soft mechanical sensors for pressure, strain, shear, and vibration, we discussed the recent material and device design innovations for achieving several important properties, including flexibility and stretchability, bioresorbability and biodegradability, self-healing properties, breathability, transparency, wireless communication capabilities, and high-density integration. We then went on to discuss the current research state of the use of such novel soft mechanical sensors in wearable and implantable applications, based on which future research needs were further discussed. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Implantable Materials and Surgical Technologies > Nanomaterials and Implants.

A Reconfigurable Soft Linkage Robot via Internal "Virtual" Joints

Traditional robots derive their capabilities of movement through rigid structural "links" and discrete actuated "joints." Alternatively, soft robots are composed of flexible materials that permit movement across a continuous range of their body and appendages and thus are not restricted in where they can bend. While trade-offs between material choices may restrain robot functionalities within a narrow spectrum, we argue that bridging the functional gaps between soft and hard robots can be achieved from a hybrid design approach that utilizes both the reconfigurability and the controllability of traditional soft and hard robot paradigms. In this study, we present a hybrid robot with soft inflated "linkages," and rigid internal joints that can be spatially reconfigured. Our method is based on the geometric pinching of an inflatable beam to form mechanical pinch-joints connecting the inflated robot linkages. Such joints are activated and controlled via internal motorized modules that can be relocated for on-demand joint-linkage configurations. We demonstrate two applications that utilize joint reconfigurations: a deployable robot manipulator and a terrestrial crawling robot with tunable gaits.

Femtosecond Laser Fabrication of High-Linearity Liquid Metal-Based Flexible Strain Sensor

Liquid metal (LM) is widely used in flexible electronic devices due to its excellent metallic conductivity and ductility. However, the fabrication of LM flexible strain sensors with high sensitivity and linearity is still a huge challenge, since the resistance of LM does not change much with strain. Here, a highly sensitive and linear fully flexible strain sensor with a resistive sensing function is proposed. The sensor comprises an Fe-doped liquid metal (Fe-LM) electrode for enhanced performance. The design and manufacturing of flexible strain sensors are based on the technology of controlling surface wettability by femtosecond laser micro/nano-processing. A supermetalphobic microstructure is constructed on a polydimethylsiloxane (PDMS) substrate to achieve the selection adhesion of Fe-LM on the PDMS substrate. The Fe-LM-based flexible strain sensor has high sensitivity and linearity, a gauge factor (GF) up to 1.18 in the strain range of 0-100%, excellent linearity with an R2 of 0.9978, a fast response time of 358 ms, and an excellent durability of more than 2400 load cycles. Additionally, the successful monitoring of human body signals demonstrates the potential of our developed flexible strain sensor in wearable monitoring applications.

A Modular Soft Sensing Skin for Fast Measurement of Wing Deformation in Small Unmanned Aerial Vehicles

Insects, bats, and small birds show outstanding flight performance even under complex atmospheric conditions, which is partially due to the ability of these natural fliers to sense and react to disturbances quickly. These biological systems often use large numbers of sensors arrayed across their bodies to detect disturbances, but previous efforts to use large arrays of sensors in engineered fliers have typically resulted in slow responses due to the need to scan and process data from the large number of sensors. To address the challenges of capturing disturbances in a large sensing array with low latency, this work proposes and demonstrates a modular soft sensing system to quickly detect disturbances in small unmanned aerial vehicles. A large array of soft strain sensors with high sensing resolution covers the entire wingspan, providing rich information on wing deformation. Owing to the modular design, decentralized computation enables the sensing system to efficiently manage sensor data, resulting in sufficiently fast sampling to capture wing dynamics while all 32 sensors embedded in the modular soft sensing skin are used. This hardware architecture also results in significantly reduced noise in the sensing system, leading to a high signal-to-noise ratio. These methods can ultimately enable fast and reliable control of both soft and rigid robotic systems using large arrays of soft sensors.

Recent Progress and Challenges of Implantable Biodegradable Biosensors

Implantable biosensors have evolved to the cutting-edge technology of personalized health care and provide promise for future directions in precision medicine. This is the reason why these devices stand to revolutionize our approach to health and disease management and offer insights into our bodily functions in ways that have never been possible before. This review article tries to delve into the important developments, new materials, and multifarious applications of these biosensors, along with a frank discussion on the challenges that the devices will face in their clinical deployment. In addition, techniques that have been employed for the improvement of the sensitivity and specificity of the biosensors alike are focused on in this article, like new biomarkers and advanced computational and data communicational models. A significant challenge of miniaturized in situ implants is that they need to be removed after serving their purpose. Surgical expulsion provokes discomfort to patients, potentially leading to post-operative complications. Therefore, the biodegradability of implants is an alternative method for removal through natural biological processes. This includes biocompatible materials to develop sensors that remain in the body over longer periods with a much-reduced immune response and better device longevity. However, the biodegradability of implantable sensors is still in its infancy compared to conventional non-biodegradable ones. Sensor design, morphology, fabrication, power, electronics, and data transmission all play a pivotal role in developing medically approved implantable biodegradable biosensors. Advanced material science and nanotechnology extended the capacity of different research groups to implement novel courses of action to design implantable and biodegradable sensor components. But the actualization of such potential for the transformative nature of the health sector, in the first place, will have to surmount the challenges related to biofouling, managing power, guaranteeing data security, and meeting today's rules and regulations. Solving these problems will, therefore, not only enhance the performance and reliability of implantable biodegradable biosensors but also facilitate the translation of laboratory development into clinics, serving patients worldwide in their better disease management and personalized therapeutic interventions.

Triboelectric micro-flexure-sensitive fiber electronics

Developing fiber electronics presents a practical approach for establishing multi-node distributed networks within the human body, particularly concerning triboelectric fibers. However, realizing fiber electronics for monitoring micro-physiological activities remains challenging due to the intrinsic variability and subtle amplitude of physiological signals, which differ among individuals and scenarios. Here, we propose a technical approach based on a dynamic stability model of sheath-core fibers, integrating a micro-flexure-sensitive fiber enabled by nanofiber buckling and an ion conduction mechanism. This scheme enhances the accuracy of the signal transmission process, resulting in improved sensitivity (detectable signal at ultra-low curvature of 0.1 mm-1; flexure factor >21.8% within a bending range of 10°.) and robustness of fiber under micro flexure. In addition, we also developed a scalable manufacturing process and ensured compatibility with modern weaving techniques. By combining precise micro-curvature detection, micro-flexure-sensitive fibers unlock their full potential for various subtle physiological diagnoses, particularly in monitoring fiber upper limb muscle strength for rehabilitation and training.

A double-crack structure for bionic wearable strain sensors with ultra-high sensitivity and a wide sensing range

Flexible strain sensors are crucial in fully monitoring human motion, and they should have a wide sensing range and ultra-high sensitivity. Herein, inspired by lyriform organs, a flexible strain sensor based on the double-crack structure is designed. An MXene layer and an Au layer with cracks are constructed on both sides of the insulated polydimethylsiloxane (PDMS) film, forming an equivalent parallel circuit that guarantees the integrity of the conductive path under a large strain. The rapid disconnection of the crack junctions causes a significant change in the resistance value. Due to the effect of cracks on the conductive path, the sensitivity of the sensor is largely improved. Benefiting from the double-crack structure, the as-obtained sensor shows ultra-high sensitivity (maximum gauge factor of up to 14 373.6), a wide working range (up to 21%), a fast response time (183 ms) and excellent dynamical stability (almost no performance loss after 1000 stretching cycles and different frequency cycles). In practical applications, the sensor is applied to different parts of the human body to sense the deformation of the skin, demonstrating its great potential application value in human physiological detection and the human-machine interaction. This study can provide new ideas for preparing high-performance flexible strain sensors.

Integrating In-Plane Thermoelectricity and Out-Plane Piezoresistivity for Fully Decoupled Temperature-Pressure Sensing

A flexible sensor that simultaneously senses temperature and pressure is crucial in various fields, such as human-machine interaction, artificial intelligence, and biomedical applications. Previous research has mainly focused on single-function flexible sensors for e-skins or smart devices, and integrated bimodal sensing of temperature and pressure without complex crosstalk decoupling algorithms remains challenging. In this work, a flexible bimodal sensor is proposed that utilizes spatial orthogonality between in-plane thermoelectricity and out-plane piezoresistivity, which enables fully decoupled temperature-pressure sensing. The proposed bimodal sensor exhibits a high sensitivity of 281.46 µV K-1 for temperature sensing and 2.181 kPa-1 for pressure sensing. In the bimodal sensing mode, the sensor exhibits negligible mutual interference, providing a measurement error of ± 7% and ± 8% for temperature and pressure, respectively, within a 120 kPa pressure range and a 40 K temperature variation. Additionally, simultaneous spatial mapping of temperature and pressure with a bimodal sensor array enables contact shape identification with enhanced accuracy beyond the limit imposed by the number of sensing units. The proposed integrated bimodal sensing strategy does not require complex crosstalk decoupling algorithms, which represents a significant advancement in flexible sensors for applications that necessitate simultaneous sensing of temperature and pressure.

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

All-organic transparent plant e-skin for noninvasive phenotyping

Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.

Achieving a Wide-Range Linear Piezoresistive Response in Electrowritten Soft-Hard Polymer Blends via Salami-Inspired Heterostructure Design

Flexible electronics capable of acquiring high-precision signals are in great demand for the development of the internet of things and intelligent artificial. However, it is currently a challenge to simultaneously achieve high signal linearity and sensitivity for stretchable resistive sensors over a wide strain range toward advanced application scenarios requiring high signal accuracy, e.g., sophisticated physiological signal discrimination and displacement measurement. Herein, a film strain sensor, which has an electrical and mechanical dual heterostructure, was fabricated via a direct near-field electrowriting and molecule-guided in situ growth of silver nanoparticles with different concentrations on high-modulus polystyrene domains and low-modulus styrene-butadiene copolymers with a salami-like morphology. Mechanism analyses from both theoretical and experimental investigations reveal that the salami-like heteromodulus microstructure regulates microcrack propagation routes, while the heteroconductivity changes the electron transport paths and amplifies the resistance increase during crack propagation. Therefore, the as-designed strain sensor shows a linear resistive response within ca. 70% strain with a gauge factor of 25, unveiling a simple and scalable strategy for trading off signal linearity and sensitivity over a wide strain range for the fabrication of high-performance linear strain sensors.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Deep-learning enabled smart insole system aiming for multifunctional foot-healthcare applications

Real-time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot-related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real-time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning-enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long-term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise-fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Negative Enthalpy Variation Drives Rapid Recovery in Thermoplastic Elastomer

The mechanism behind the resilience of polymeric materials, typically attributed to the well-established entropy elasticity, often ignores the contribution of enthalpy variation (ΔH), because it is based on the assumption of an ideal chain. However, this model does not fully account for the reduced resilience of thermoplastic polyurethane (TPU) during long-range deformation, which is mainly caused by the dynamics of physical crosslink networks. Such reduction is undesirable for long-range stretchable TPU considering its wide application range. Therefore, a negative ΔH effect is established in this work to facilitate instant recovery in long-range stretchable TPU, achieved by constructing a reversible interim interface via strain-induced phase separation. Consequently, the newly constructed dual soft segmental TPU shows resilience efficiency exceeding 95%, surpassing many synthetic high-performance TPUs with typical efficiencies below 80%, and comparable to biomaterials. Moreover, a remarkable hysteresis loop with a ratio exceeding 50%, makes it a viable candidate for applications such as artificial ligaments or buffer belts. The research also clarifies structural factors influencing resilience, including the symmetry of the dual soft segments and the content of hard segments, offering valuable insights for the design of highly resilient long-range stretchable elastomers.

Linear Strain Sensors via a Spatial Heteromodulus Tricontinuous Structure Design for High-Resolution Recording of Snoring Breath

Porous conductive elastomer composites are very attractive for designing flexible and air-permeable mechanical sensors for healthcare, while it is challenging to achieve a linear and sensitive electromechanical response over a wide strain range for high-resolution recording of physiological activities and body motions. Here, a scalable strategy is developed to construct porous elastomer composites with a bamboo-shaped heteromodulus microstructure in the pores for the fabrication of linear stretchable strain sensors. Such a spatial heteromodulus microstructure is fabricated via phase separation and selective location of high-modulus phase during melt compounding of elastomers and thermoplastics, together with green etching of the water-soluble plastic in the tricontinuous elastomer composites. The bamboo-shaped heteromodulus microstructure is constructed on the pore struts via the fracture of a high-modulus polymer self-assembled on the pore surface and relaxation recovery of the elastomer matrix after prestretching, which blocks the propagation of cut-through microcracks upon stretching. The composites with super low resistance after in situ growth of silver nanoparticles sustain up to 110% tensile strain with a linear and sensitive electromechanical response, demonstrating potential applications in discriminating respiration status and monitoring snoring breath. This work unveils a new approach to fabricate high-performance air-permeable strain sensors in a simple and scalable way.

Passive Internet of Events Enabled by Broadly Compatible Self-Powered Visualized Platform Toward Real-Time Surveillance

Surveillance is an intricate challenge worldwide especially in those complicated environments such as nuclear plants, banks, crowded areas, barns, etc. Deploying self-powered wireless sensor nodes can increase the system's event detection capabilities by collecting environmental changes, while the incompatibility among components (energy harvesters, sensors, and wireless modules) limits their application. Here, a broadly compatible self-powered visualized platform (SPVP) is reported to construct a passive internet of events (IoE) network for surveillance systems. By encoding electric signals into reference and working LEDs, SPVP can visualize resistance change generated by commercial resistive sensors with a broad working range (<107  Ω) and the transmission distance is up to 30 meters. Visible light signals are captured by surveillance cameras and processed by the cloud to achieve real-time event monitoring and identification, which forms the passive IoE network. It is demonstrated that the passive-IoE-based surveillance system can detect intrusion, theft, fire alarm, and distress signals quickly (30 ms) for 106 cycles. Moreover, the confidential information can be encrypted by SPVPs and accessed through a phone application. This universal scheme may have huge potential for the construction of safe and smart cities.

A Selective-Response Hypersensitive Bio-Inspired Strain Sensor Enabled by Hysteresis Effect and Parallel Through-Slits Structures

Flexible strain sensors are promising in sensing minuscule mechanical signals, and thereby widely used in various advanced fields. However, the effective integration of hypersensitivity and highly selective response into one flexible strain sensor remains a huge challenge. Herein, inspired by the hysteresis strategy of the scorpion slit receptor, a bio-inspired flexible strain sensor (BFSS) with parallel through-slit arrays is designed and fabricated. Specifically, BFSS consists of conductive monolayer graphene and viscoelastic styrene-isoprene-styrene block copolymer. Under the synergistic effect of the bio-inspired slit structures and flexible viscoelastic materials, BFSS can achieve both hypersensitivity and highly selective frequency response. Remarkably, the BFSS exhibits a high gage factor of 657.36, and a precise identification of vibration frequencies at a resolution of 0.2 Hz through undergoing different morphological changes to high-frequency vibration and low-frequency vibration. Moreover, the BFSS possesses a wide frequency detection range (103 Hz) and stable durability (1000 cycles). It can sense and recognize vibration signals with different characteristics, including the frequency, amplitude, and waveform. This work, which turns the hysteresis effect into a "treasure," can provide new design ideas for sensors for potential applications including human-computer interaction and health monitoring of mechanical equipment.

Machine Learning-Enabled Intelligent Gesture Recognition and Communication System Using Printed Strain Sensors

Gesture contains abundant and complicated information in daily life; as a consequence, gesture recognition attracts a wide range of application prospects and academic values as an important way of achieving human-machine interactions (HMIs). Here, we report an intelligent system consisting of a smart glove made by printed CNT-graphene/PDMS strain sensors. The smart glove shows excellent fitness, comfort, and lightness for human hands. Inspired by machine learning strategies, several objects and gestures can be well classified and implemented by a customized artificial neural network. Several data sets of different sign language gestures and object-grabbing gestures were established, and the result shows that the intelligent system can achieve an average accuracy of 97% and up to 99.4% for a number of gesture groups. Moreover, a robot hand is connected to this system, which is able to react to the motion of human hands with certain gestures where simple sign communication is achieved. These features provide a feasible practical application scheme for gesture recognition in HMIs.

Water-Resistant Conductive Gels toward Underwater Wearable Sensing

Conductive gels are developing vigorously as superior wearable sensing materials due to their intrinsic conductivity, softness, stretchability, and biocompatibility, showing a great potential in many aspects of lives. However, compared to their wide application on land, it is significant yet rather challenging for traditional conductive gels to realize sensing application under water. The swelling of gels and the loss of conductive components in the aqueous environment, resulted from the diffusion across the interface, lead to structural instability and sensing performance decline. Fortunately, great efforts are devoted to improving the water resistance of conductive gels and employing them in the field of underwater wearable sensing in recent years, and some exciting achievements are obtained, which are of great significance for promoting the safety and efficiency of underwater activities. However, there is no review to thoroughly summarize the underwater sensing application of conductive gels. This review presents a brief overview of the representative design strategies for developing water-resistant conductive gels and their diversified applications in the underwater sensing field as wearable sensors. Finally, the ongoing challenges for further developing water-resistant conductive gels for underwater wearable sensing are also discussed along with recommendations for the future.

Electronic exoneuron based on liquid metal for the quantitative sensing of the augmented somatosensory system

The increasing demands in augmented somatosensory have promoted quantitative sensing to be an emerging need for athletic training/performance evaluation and physical rehabilitation. Neurons for the somatosensory system in the human body can capture the information of movements in time but only qualitatively. This work presents an electronic Exo-neuron (EEN) that can spread throughout the limbs for realizing augmented somatosensory by recording both muscular activity and joint motion quantitatively without site constraints or drift instability, even in strenuous activities. Simply based on low-cost liquid metal and clinically used adhesive elastomer, the EEN could be easily fabricated in large areas for limbs. It is thin (~120 μm), soft, stretchable (>500%), and conformal and further shows wide applications in sports, rehabilitation, health care, and entertainment.

Superflexible Artificial Soft Wood

Soft woods have attracted enormous interest due to their anisotropic cellular microstructure and unique flexibility. The conventional wood-like materials are usually subject to the conflict between the superflexibility and robustness. Inspired by the synergistic compositions of soft suberin and rigid lignin of cork wood which has good flexibility and mechanical robustness, an artificial soft wood is reported by freeze-casting the soft-in-rigid (rubber-in-resin) emulsions, where the carboxy nitrile rubber confers softness and rigid melamine resin provides stiffness. The subsequent thermal curing induces micro-scale phase inversion and leads to a continuous soft phase strengthened by interspersed rigid ingredients. The unique configuration ensures crack resistance, structural robustness and superb flexibility, including wide-angle bending, twisting, and stretching abilities in various directions, as well as excellent fatigue resistance and high strength, overwhelming the natural soft wood and most wood-inspired materials. This superflexible artificial soft wood represents a promising substrate for bending-insensitive stress sensors.

Investigating Changes in Muscle Coordination During Cycling with Soft Wearable Strain Sensors Sensitive to Muscle Deformation

Continuous monitoring of muscle coordination can provide valuable information regarding an individual's performance during physical activities. For example, changes in muscle coordination can indicate muscle fatigue during exhaustive exercise or can be used to track the rehabilitation progress of patients post-injury. Traditional methods to evaluate coordination often focus solely on measuring muscle activation with electromyography, ignoring timing changes of the resultant force produced by the activated muscle. Setups designed to evaluate force directly to study muscle coordination are often limited by either hyper-constrained settings or cost-prohibitive hardware. In this paper, we employ wearable, ultra-sensitive soft strain sensors that track muscle deformation for estimating changes in muscle coordination during cycling at different cadences and to exhaustion. The results were compared to muscle activation timing measured by electromyography and peak force timing measured by a cycle ergometer. We demonstrate that with an increase in cadence, the soft strain sensor and ergometer timing metrics align more closely than those measured by electromyography. We also demonstrate how muscle coordination is altered with the onset of fatigue during cycling to exhaustion.

Soft robot-mediated autonomous adaptation to fibrotic capsule formation for improved drug delivery

The foreign body response impedes the function and longevity of implantable drug delivery devices. As a dense fibrotic capsule forms, integration of the device with the host tissue becomes compromised, ultimately resulting in device seclusion and treatment failure. We present FibroSensing Dynamic Soft Reservoir (FSDSR), an implantable drug delivery device capable of monitoring fibrotic capsule formation and overcoming its effects via soft robotic actuations. Occlusion of the FSDSR porous membrane was monitored over 7 days in a rodent model using electrochemical impedance spectroscopy. The electrical resistance of the fibrotic capsule correlated to its increase in thickness and volume. Our FibroSensing membrane showed great sensitivity in detecting changes at the abiotic/biotic interface, such as collagen deposition and myofibroblast proliferation. The potential of the FSDSR to overcome fibrotic capsule formation and maintain constant drug dosing over time was demonstrated in silico and in vitro. Controlled closed loop release of methylene blue into agarose gels (with a comparable fold change in permeability relating to 7 and 28 days in vivo) was achieved by adjusting the magnitude and frequency of pneumatic actuations after impedance measurements by the FibroSensing membrane. By sensing fibrotic capsule formation in vivo, the FSDSR will be capable of probing and adapting to the foreign body response through dynamic actuation changes. Informed by real-time sensor signals, this device offers the potential for long-term efficacy and sustained drug dosing, even in the setting of fibrotic capsule formation.

Monitoring blood pressure and cardiac function without positioning via a deep learning-assisted strain sensor array

Continuous and reliable monitoring of blood pressure and cardiac function is of great importance for diagnosing and preventing cardiovascular diseases. However, existing cardiovascular monitoring approaches are bulky and costly, limiting their wide applications for early diagnosis. Here, we developed an intelligent blood pressure and cardiac function monitoring system based on a conformal and flexible strain sensor array and deep learning neural networks. The sensor has a variety of advantages, including high sensitivity, high linearity, fast response and recovery, and high isotropy. Experiments and simulation synergistically verified that the sensor array can acquire high-precise and feature-rich pulse waves from the wrist without precise positioning. By combining high-quality pulse waves with a well-trained deep learning model, we can monitor blood pressure and cardiac function parameters. As a proof of concept, we further constructed an intelligent wearable system for real-time and long-term monitoring of blood pressure and cardiac function, which may contribute to personalized health management, precise and early diagnosis, and remote treatment.

Flexible BaTiO3-PDMS Capacitive Pressure Sensor of High Sensitivity with Gradient Micro-Structure by Laser Engraving and Molding

The significant potential of flexible sensors in various fields such as human health, soft robotics, human-machine interaction, and electronic skin has garnered considerable attention. Capacitive pressure sensor is popular given their mechanical flexibility, high sensitivity, and signal stability. Enhancing the performance of capacitive sensors can be achieved through the utilization of gradient structures and high dielectric constant media. This study introduced a novel dielectric layer, employing the BaTiO3-PDMS material with a gradient micro-cones architecture (GMCA). The capacitive sensor was constructed by incorporating a dielectric layer GMCA, which was fabricated using laser engraved acrylic (PMMA) molds and flexible copper-foil/polyimide-tape electrodes. To examine its functionality, the prepared sensor was subjected to a pressure range of 0-50 KPa. Consequently, this sensor exhibited a remarkable sensitivity of up to 1.69 KPa-1 within the pressure range of 0-50 KPa, while maintaining high pressure-resolution across the entire pressure spectrum. Additionally, the pressure sensor demonstrated a rapid response time of 50 ms, low hysteresis of 0.81%, recovery time of 160 ms, and excellent cycling stability over 1000 cycles. The findings indicated that the GMCA pressure sensor, which utilized a gradient structure and BaTiO3-PDMS material, exhibited notable sensitivity and a broad linear pressure range. These results underscore the adaptability and viability of this technology, thereby facilitating enhanced flexibility in pressure sensors and fostering advancements in laser manufacturing and flexible devices for a wider array of potential applications.

Paintable Silicone-Based Corrugated Soft Elastomeric Capacitor for Area Strain Sensing

Recent advances in soft polymer materials have enabled the design of soft machines and devices at multiple scales. Their intrinsic compliance and robust mechanical properties and the potential for a rapid scaling of the production process make them ideal candidates for flexible and stretchable electronics and sensors. Large-area electronics (LAE) made from soft polymer materials that are capable of sustaining large deformations and covering large surfaces and are applicable to complex and irregular surfaces and transducing deformations into readable signals have been explored for structural health monitoring (SHM) applications. The authors have previously proposed and developed an LAE consisting of a corrugated soft elastomeric capacitor (cSEC). The corrugation is used to engineer the directional strain sensitivity by using a thermoplastic styrene-ethylene-butadiene-styrene (SEBS). A key limitation of the SEBS-cSEC technology is the need of an epoxy for reliable bonding of the sensor onto the monitored surface, mainly attributable to the sensor's fabrication process that comprises a solvent that limits its direct deployment through a painting process. Here, with the objective to produce a paintable cSEC, we study an improved solvent-free fabrication method by using a commercial room-temperature-vulcanizing silicone as the host matrix. The matrix is filled with titania particles to form the dielectric layer, yielding a permittivity of 4.05. Carbon black powder is brushed onto the dielectric and encapsulated with the same silicone to form the conductive stretchable electrodes. The sensor is deployed by directly painting a layer of the silicone onto the monitored surface and then depositing the parallel plate capacitor. The electromechanical behavior of the painted silicone-cSEC was characterized and exhibited good linearity, with an R2 value of 0.9901, a gauge factor of 1.58, and a resolution of 70 με. This resolution compared well with that of the epoxied SEBS-cSEC reported in previous work (25 με). Its performance was compared against that of its more mature version, the SEBS-cSEC, in a network configuration on a cantilever plate subjected to a step-deformation and to free vibrations. Results showed that the performance of the painted silicone-sCEC compared well with that of the SEBS-cSEC, but that the use of a silicone paint instead of an epoxy could be responsible for larger noise and the under-estimation of the dominating frequency by 6.7%, likely attributable to slippage.

A soft, self-sensing tensile valve for perceptive soft robots

Soft inflatable robots are a promising paradigm for applications that benefit from their inherent safety and adaptability. However, for perception, complex connections of rigid electronics both in hardware and software remain the mainstay. Although recent efforts have created soft analogs of individual rigid components, the integration of sensing and control systems is challenging to achieve without compromising the complete softness, form factor, or capabilities. Here, we report a soft self-sensing tensile valve that integrates the functional capabilities of sensors and control valves to directly transform applied tensile strain into distinctive steady-state output pressure states using only a single, constant pressure source. By harnessing a unique mechanism, "helical pinching", we derive physical sharing of both sensing and control valve structures, achieving all-in-one integration in a compact form factor. We demonstrate programmability and applicability of our platform, illustrating a pathway towards fully soft, electronics-free, untethered, and autonomous robotic systems.

Piezoresistive Free-standing Microfiber Strain Sensor for High-resolution Battery Thickness Monitoring

Highly sensitive microfiber strain sensors are promising for the detection of mechanical deformations in applications where limited space is available. In particular for in situ battery thickness monitoring where high resolution and low detection limit are key requirements. Herein, the realization of a highly sensitive strain sensor for in situ lithium-ion (Li-ion) battery thickness monitoring is presented. The compliant fiber-shaped sensor is fabricated by an upscalable wet-spinning method employing a composite of microspherical core-shell conductive particles embedded in an elastomer. The electrical resistance of the sensor changes under applied strain, exhibiting a high strain sensitivity and extremely low strain detection limit of 0.00005 with high durability of 10 000 cycles. To demonstrate the accuracy and ease of applicability of this sensor, the real-time thickness change of a Li-ion battery pouch cell is monitored during the charge and discharge cycles. This work introduces a promising approach with the least material complexity for soft microfiber strain gauges.

Skin-Inspired Ultra-Tough Supramolecular Multifunctional Hydrogel Electronic Skin for Human-Machine Interaction

Multifunctional supramolecular ultra-tough bionic e-skin with unique durability for human-machine interaction in complex scenarios still remains challenging. Herein, we develop a skin-inspired ultra-tough e-skin with tunable mechanical properties by a physical cross-linking salting-freezing-thawing method. The gelling agent (β-Glycerophosphate sodium: Gp) induces the aggregation and binding of PVA molecular chains and thereby toughens them (stress up to 5.79 MPa, toughness up to 13.96 MJ m-3). Notably, due to molecular self-assembly, hydrogels can be fully recycled and reprocessed by direct heating (100 °C for a few seconds), and the tensile strength can still be maintained at about 100% after six recoveries. The hydrogel integrates transparency (> 60%), super toughness (up to 13.96 MJ m-3, bearing 1500 times of its own tensile weight), good antibacterial properties (E. coli and S. aureus), UV protection (Filtration: 80%-90%), high electrical conductivity (4.72 S m-1), anti-swelling and recyclability. The hydrogel can not only monitor daily physiological activities, but also be used for complex activities underwater and message encryption/decryption. We also used it to create a complete finger joint rehabilitation system with an interactive interface that dynamically presents the user's health status. Our multifunctional electronic skin will have a profound impact on the future of new rehabilitation medical, human-machine interaction, VR/AR and the metaverse fields.

Ultrathin, soft, radiative cooling interfaces for advanced thermal management in skin electronics

Thermal management plays a notable role in electronics, especially for the emerging wearable and skin electronics, as the level of integration, multifunction, and miniaturization of such electronics is determined by thermal management. Here, we report a generic thermal management strategy by using an ultrathin, soft, radiative-cooling interface (USRI), which allows cooling down the temperature in skin electronics through both radiative and nonradiative heat transfer, achieving temperature reduction greater than 56°C. The light and intrinsically flexible nature of the USRI enables its use as a conformable sealing layer and hence can be readily integrated with skin electronics. Demonstrations include passive cooling down of Joule heat for flexible circuits, improving working efficiency for epidermal electronics, and stabling performance outputs for skin-interfaced wireless photoplethysmography sensors. These results offer an alternative pathway toward achieving effective thermal management in advanced skin-interfaced electronics for multifunctionally and wirelessly operated health care monitoring.

Extremely Robust and Multifunctional Nanocomposite Fibers for Strain-Unperturbed Textile Electronics

Textile electronics are needed that can achieve strain-unaltered performance when they undergo irregular and repeated strain deformation. Such strain-unaltered textile electronics require advanced fibers that simultaneously have high functionalities and extreme robustness as fabric materials. Current synthetic nanocomposite fibers based on inorganic matrix have remarkable functionalities but often suffer from low robustness and poor tolerance against crack formation. Here, we present a design for a high-performance multifunctional nanocomposite fiber that is mechanically and electrically robust, which was realized by crosslinking titanium carbide (MXene) nanosheets with a slide-ring polyrotaxane to form an internal mechanically-interlocked network. This inorganic matrix nanocomposite fiber featured distinct strain-hardening mechanical behavior and exceptional load-bearing capability (toughness approaching 60 MJ m^-3 and ductility over 27%). It retained 100% of its ductility after cyclic strain loading. Moreover, the high electrical conductivity (>1.1 × 10⁵ S m^-1 ) and electrochemical performance (>360 F cm^-3 ) of the nanocomposite fiber can be well retained after subjecting the fiber to extensive (>25% strain) and long-term repeated (10 000 cycles) dimensional changes. Such superior robustness allowed for the fabrication of the nanocomposite fibers into various robust wearable devices, such as textile-based electromechanical sensors with strain-unalterable sensing performance and fiber-shaped supercapacitors with invariant electrochemical performance for 10 000 strain loading cycles.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing

As an important part of human-machine interfaces, piezoelectric voice recognition has received extensive attention due to its unique self-powered nature. However, conventional voice recognition devices exhibit a limited response frequency band due to the intrinsic hardness and brittleness of piezoelectric ceramics or the flexibility of piezoelectric fibers. Here, we propose a cochlear-inspired multichannel piezoelectric acoustic sensor (MAS) based on gradient PVDF piezoelectric nanofibers for broadband voice recognition by a programmable electrospinning technique. Compared with the common electrospun PVDF membrane-based acoustic sensor, the developed MAS demonstrates the greatly 300%-broadened frequency band and the substantially 334.6%-enhanced piezoelectric output. More importantly, this MAS can serve as a high-fidelity auditory platform for music recording and human voice recognition, in which the classification accuracy rate can reach up to 100% in coordination with deep learning. The programmable bionic gradient piezoelectric nanofiber may provide a universal strategy for the development of intelligent bioelectronics.