The fibrous temperature sensor with excellent flexibility, comfort, and ease of integration into fabrics is particularly suitable for body temperature monitoring. However, the detection stability of existing fibrous temperature sensors is greatly affected by external factors such as pressing, bending, twisting, pH, humidity, and human movement. Here, we propose a fibrous temperature sensor based on an optimized scalable ionic liquid immersion process. The proposed sensor exhibited excellent temperature response characteristics, good linearity, a high sensitivity of 2.61%/°C, and can resist disturbances caused by pressing, bending, and twisting deformation. Moreover, it can work normally in acidic and alkaline environments with good reliability and stability. To demonstrate its application potential, we successfully integrated the sensor into firefighter suits, sports wristbands, and infant suits for real-time temperature monitoring and early warning.

Self-powered sensing technology plays a key role in autonomous and portable systems, with applications in health monitoring and robotics. These sensors, which do not rely on external power sources, offer stable, continuous data acquisition for real-time monitoring and complex interactions. For instance, triboelectric nanogenerators have enabled self-powered wearable sensors to monitor vital signs such as heart beat rate and respiration by converting body movement into electrical energy, eliminating the need for batteries. Despite their advantages, challenges remain in large-scale manufacturing, miniaturization, and multifunctional integration. Overcoming these challenges may require innovative advances in novel materials, intelligent algorithms, and multifunctional integration strategies. This perspective summarizes recent advances and existing challenges in self-powered sensing technologies for health monitoring and robotics applications, and provides an outlook on their future development.

Bionic water strider robots (BWSRs) have been a hot spot in research due to their unique biological inspiration and versatile applications in environmental monitoring. However, creating aquatic micro-robots with combined structure and function that can walk freely and perceive environmental information on water surfaces remains a challenge. Herein, self-propelled and integrated aquatic microrobots that can be remotely controlled by light and wireless environment detection are designed and fabricated by combining the actuating polydimethylsiloxane (PDMS)/carbon nanotube (CNT) substrate with heterogeneous wettability and the fully integrated wireless sensors. The substrate generates thrust on water through the photothermal Marangoni effect, serving as the propulsion system for this water-gliding microrobot. The heterogeneous wettability design features the robot with a hydrophobic body and four hydrophilic footpads. The hydrophobic body creates an air gap between the device and water, providing waterproof protection for electronics, making the robot move faster with smaller power and enabling greater load-carrying capacity. The hydrophilic footpads help the robot stand firmly on water, resisting overturn by waves and also enable modular assembly between robots. The load ability of a typical robot is 1,089% of its weight, resulting in the manufacture of microrobots with fully integrated visible light sensors and Bluetooth (BT) chips that can be steered by near-infrared laser. Hopefully, this strategy shall help to develop untethered aquatic robots with sophisticated actuation and wireless sensing for complex aquatic environments.

Electronic skins (e-skins) are desired to perceive both the intensity and spatial distribution of applied pressures. Despite the continuous progress in the design of high-performance individual pressure sensors, the acquisition of the locational information of pressures still mostly relies on the preparation of high-density sensor matrixes within e-skins, which dramatically increases device complexity and hinders the straightforward human-machine interaction. In recent years, the integration of optical pressure visualization units within e-skins has been raised as an alternative strategy for obtaining pressure distribution. By utilizing pressure-induced light emission and color change, the applied pressure could be visualized directly through the distinct optical signals, eliminating the necessity of additional data processing and display modules. In this perspective, the main strategies to achieve pressure visualization in e-skins are introduced, including their mechanism, device layout, materials, and applications. The challenges and prospects of this emerging field are also discussed.

Sleeping head monitoring through an intelligent pillow is crucial in warning of disastrous diseases such as suffocation during snoring. However, flexible sensors are still inferior due to limited sensing performance and impermeable wearing discomfort. Herein we develop a flexible pressure sensor by constructing both decorated MXene piezoresistive network and printed interdigital silver electrodes on a dust-free paper platform. The intrinsic paper fabric framework provides good permeability for comfortable skin-attaching, and the microstructured MXene on paper offers good sensing performance with high sensitivity (16.7 kPa^-1 < 20 kPa), a wide detection range (~100 kPa), and fast response time (~50 ms). As a proof of concept, the single sensor unit integrated into an airbag pillow for different types of snoring monitoring and five sensor arrays for sleeping head posture recognition by machine learning are both demonstrated. The proposed paper-based sensor, along with the intelligent airbag pillow, provides a promising approach for sleep monitoring in a comfortable unrestricted way.

Human skin-inspired neuromorphic sensors have shown great potential in revolutionizing machines to perceive and interact with environments. Human skin is a remarkable organ, capable of detecting a wide variety of stimuli with high sensitivity and adaptability. To emulate these complex functions, skin-inspired neuromorphic sensors have been engineered with flexible or stretchable materials to sense pressure, temperature, texture, and other physical or chemical factors. When integrated with neuromorphic computing systems, which emulate the brain’s ability to process sensory information efficiently, these sensors can further enable real-time, context-aware responses. This study summarizes the state-of-the-art research on skin-inspired sensors and the principles of neuromorphic computing, exploring their synergetic potential to create intelligent and adaptive systems for robotics, healthcare, and wearable technology. Additionally, we discuss challenges in material/device development, system integration, and computational frameworks of human skin-inspired neuromorphic sensors, and highlight promising directions for future research.

Kirigami, known for its ultra-softness, ultra-lightness, and high stretchability, is at the forefront of research in advanced materials and structural design. However, its inherent flexibility and sensitivity pose significant challenges for mechanical characterization, as conventional rigid-body assumptions are inadequate. Key hurdles include developing flexible tensile mechanics and designing high-curvature structures to prevent fracture at cut edges. Despite advancements in nanoscale synthesis and large-scale deployable kirigami systems that enhance geometric and material design, the lack of robust models to describe complex in-plane and out-of-plane buckling under extreme conditions hampers further theoretical and applied progress. Current reciprocal mechanics theories struggle to capture the nonlinearities, multi-stability, and asymmetry characteristic of kirigami deformation. Static nonreciprocity offers a promising alternative by distinguishing forward and reverse mechanical responses, breaking time-reversal symmetry, and providing deeper mechanical insights. Moving forward, establishing a framework based on nonreciprocal properties will be essential to overcoming existing challenges, driving breakthroughs in kirigami mechanics, and enabling innovative applications in areas such as soft robotics, deployable systems, and flexible electronics.

Skeletal muscles, as the primary actuators for voluntary limb motions, achieve motion dexterity and endurance at the cost of majority of metabolic energy. Muscle energetics provide a powerful framework for examining motion skills, serving as fundamental mechanisms for converting metabolic energy into effective work to optimize motion performance through muscle synergy. However, existing energy sensing methods are sensitive to physiological, psychological, and environmental disturbances, making it challenging to monitor the energetic dynamics of muscle synergy. Inspired by the characteristics of muscle excitation and contraction, this study proposes a neural-mechanical sensing method to perceive muscle work by integrating the myoelectric and capacitive measurements that are indicative of muscle forces and contraction displacements. The proposed sensing method is validated through the weight lifting tests, comparing results against the dynamic analysis and muscle oxygen consumption. To the best of our knowledge, this research is the first to achieve in-situ real-time wearable sensing of muscle work. It is expected to pave a practical way to study muscle energetics that is beneficial to sports science, rehabilitation medicine and robotics engineering.

Cholesteric liquid crystal networks (CLCNs) intertwined with interpenetrating polymer network (IPN) hydrogels have emerged as a promising platform for the development of optical photonic sensors. However, the integration of a multiplex biosensor within a flexible substrate for real-time human sweat analysis remains a significant challenge. Herein, we present a novel flexible CLCN-IPN-based sensor array, embedded within a soft wearable microfluidic patch, designed for the continuous monitoring of human sweat. This innovative biosensor allows optical detection of glucose, urea, and lactate with impressive limits of detection: 0.31 mM for glucose, 0.273 mM for urea, and 3.11 mM for lactate. The wearable array biosensor enables real-time analysis of sweat, offering results that can be interpreted with the naked eye, eliminating the need for complex instruments. This approach to epidermal health monitoring has the potential to inspire new advancements in wearable sensor devices, contributing to the future of smart healthcare applications.

Electronic skin (e-skin) has been widely used in various fields such as health monitoring, robotic tactile perception, and bioinspired prosthetics due to its ability to detect a wide range of signals. However, traditional flexible e-skin is limited in providing detailed information about the sensing surface and the velocity of surface fluid motion, which restricts its further applications. In this study, we successfully fabricated a bioinspired cilia-based e-skin that enables the sensing and detection of surface morphology, Braille, and airflow velocity. The bioinspired cilia exhibited a linear sensing range for static detection, with bending angles from 15° to 60°, and a frequency range of 1-25 Hz for dynamic sensing. A single cilia could accurately detect surface morphology changes as small as 0.5 mm and recognize Braille characters. Additionally, the cilia-based e-skin was capable of sensing and detecting airflow velocity. This multifunctional cilia-based e-skin integrates three major functions: static tactile sensing (10-22,000 Pa), dynamic sliding sensing (0.8-5.4 cm/s), and airflow sensing (1.8-5.7 m/s). This advancement holds promise for providing a novel approach to the multifunctional integration of flexible electronics.

Hydrogel-based moisture-electric generators (HMEGs) have emerged as a promising technology for sustainable energy harvesting by utilizing ambient moisture. This article highlights recent advancements in HMEG development, focusing on innovative hydrogel designs to enhance energy output and practical applicability. Hydrogels provide a highly efficient medium for water absorption and ion transport, but their limited moisture generation performance necessitates polymer engineering strategies. Protonation doping and the incorporation of other cations, such as sodium ions, have been shown to significantly improve electrical output. Furthermore, dual-network hydrogels enhance both mechanical robustness and energy conversion efficiency. Future research should focus on improving scalability through large-scale fabrication techniques, enhancing durability under varying environmental conditions, and optimizing hydrogel properties for wearable and implantable applications. With continued material and engineering innovations, HMEGs hold great potential for advancing self-powered electronics and sustainable energy solutions.

Conductive hydrogels have drawn significant attention as smart sensing systems for flexible electronics. However, challenges remain in fabricating multimodal electronics that simultaneously achieve ultrastretchability, conformal adhesion, environmental adaptability, self-healing, and high-performance sensing for electrophysiological signal detection. In this study, a nanocomposite organohydrogel with these features is developed by incorporating chitosan-encapsulated MXene nanosheets into a polyacrylamide network within a phytic acid (PA)/glycerol (GL)/water trisolvent system, aiming to create a multimodal sensing platform. The synergy between hydrogen bonds and electrostatic interactions endows the organohydrogel with exceptional properties, including ultrastretchability (2,800%), robust adhesion (70.6 kPa on paper), and self-healing ability. The combination of PA and GL not only enhances the organohydrogel’s environmental adaptability (-30 to 60 °C) to meet diverse application requirements but also improves its conductivity. These remarkable features enable the organohydrogel to function as a multimodal sensor capable of detecting multiple stimuli (strain and temperature) with high sensitivity and strong robustness against external disturbances. Moreover, it serves as a reliable electrode for electromyography signal detection, providing a high signal-to-noise ratio and low interfacial impedance. By integrating deep learning algorithms, the organohydrogel sensing system achieves 100% accuracy in ball sports identification, showcasing its potential for multimodal sensing platforms.