Porous-Structure-Promoted Tribo-Induced High-Performance Self-Powered Tactile Sensor toward Remote Human-Machine Interaction

Gravity-Driven Air-Liquid Interface Flexible Sensor for Human Motion Detection

This study introduces a novel gravity-driven air-liquid interface flexible sensor (GALIFS) for detecting human motions. GALIFS leverages gravity-induced liquid flow to generate angle-dependent electrical signals, eliminating the need for material deformation (e.g., stretching or compression) during operation. Unlike conventional inertial sensors (limited by rigid designs causing discomfort) or existing flexible sensors (reliant on stress-induced signals and high material durability), GALIFS overcomes these constraints through its unique gravity-driven mechanism. Furthermore, GALIFS operates without requiring perfect skin adhesion, significantly enhancing user comfort. GALIFS achieves a wide angular detection range (0°-180°) with high stability (over 16000 cycles). Additionally, it can identify a diverse range of human motions, including neck bending, spine bending, squatting, jumping, walking, and running. A real-time lying posture monitoring system for bedridden patients is further developed, showcasing its medical potential. Following successful mitigation of liquid evaporation issues, GALIFS may have significant potential for applications in various scenarios, including medical rehabilitation and sports training.

Optimized Magnetization Distribution in Body-Centered Cubic Lattice-Structured Magnetoelastomer for High-Performance 3D Force-Tactile Sensors

Flexible magnetic tactile sensors hold transformative potential in robotics and human-computer interactions by enabling precise force detection. However, existing sensors face challenges in balancing sensitivity, detection range, and structural adaptability for sensing force. This study proposed a pre-compressed magnetization method to address these limitations by amplifying the magnetoelastic effect through optimized magnetization direction distribution of the elastomer. A body-centered cubic lattice-structured magnetoelastomer featuring regular deformation under compression was fabricated via digital light processing (DLP) to validate this method. Finite element simulations and experimental analyses revealed that magnetizing the material under 60% compression strain optimized magnetization direction distribution, enhancing force-magnetic coupling. Integrating the magnetic elastomer with a hall sensor, the prepared tactile sensor demonstrated a low detection limit (1 mN), wide detection range (0.001-10 N), rapid response/recovery times (40 ms/50 ms), and durability (>1500 cycles). By using machine learning, the sensor enabled accurate 3D force prediction.

Passive Isothermal Flexible Sensor Enabled by Smart Thermal-Regulating Aerogels

Environmentally induced sensor temperature fluctuations can distort the outputs of a sensor, reducing their stability during long-term health monitoring. Here, a passive isothermal flexible sensor is proposed by using hierarchical cellulose aerogel (HCA) as the top tribonegative layer, which allows the sensor to adapt dynamic thermal environments through both radiative cooling and heat insulation. The radiative cooling effect can cool down the temperatures of a sensor in summer, while the hollow microfibers in HCA provide ultralow thermal conductivity to reduce internal heat loss in winter. The prepared passive isothermal sensor is capable of maintaining the rated working temperature over an extensive temperature range of 0-100 °C, demonstrating for gripping hot and cold objects. While monitoring human movements under direct sunlight, the temperature of a conventional sensor rose by 12.3 °C, whereas the sensor experienced an increase of only 0.3 °C. Therefore, this work presents a promising strategy for adapting to environments, enabling wearable electronics to function effectively in dynamic thermal conditions.

Anisotropic conductive eutectogels for strain sensing and triboelectric nanogeneration in extreme environments

Conductive hydrogels have attracted widespread attention for their promising application prospects in portable and flexible electronic devices. However, hydrogels commonly suffer from problems such as solvent volatilization and freezing at low temperatures. Inspired by tissues such as human muscles, tendons, and ligaments, this study proposes a facile method to produce anisotropic conductive strong and tough eutectogels through directional freezing integrated with solvent substitution (DFSS) strategy. Eutectogels with anisotropic characteristics exhibit a highly anisotropic structure, conferring distinctive anisotropic mechanical properties and electrical conductivity. The prepared anisotropic PVA-M-DES eutectogels exhibit excellent mechanical properties (high strength of 6.31 MPa, high toughness of 20.75 MJ m-3, elastic modulus of 2.36 MPa, and fracture strain of 596%), high conductivity (0.17 S m-1), excellent anti-freezing and anti-drying properties. Environment-tolerant anisotropic PVA-M-DES eutectogels can be assembled into strain sensor and triboelectric nanogenerator to achieve real-time monitoring of various human motions and have potential applications in wearable electronics, personal healthcare, energy harvesting, and human-machine interfaces.

Self-powered visualized tactile-acoustic sensor for accurate artificial perception with high brightness and record-low detection limit

The growth of the Internet of Things has focused attention on visualized sensors as a key technology. However, it remains challenging to achieve high sensing accuracy and self-power ability. Here, we propose a self-powered visualized tactile-acoustic sensor (SVTAS) based on an elaborated triboelectrification-induced electroluminescence (TIEL) unit. To date, it features a high brightness of 0.5 mW cm-2 (32 cd m-2) and a record-low detection limit of 0.5 kPa in horizontal-sliding mode. Meanwhile, the SVTAS is applicable to convert acoustic waves into TIEL signals in contact-separation mode, showing the highest response to the 44.07 Hz sound, a high signal-to-noise ratio of 8.7 dB-1, and an ultrafast response time of 0.8 ms. Furthermore, advanced artificial visualized perception systems are constructed with excellent performance in recognizing motion trajectories and human speech with different words/sentences. This work paves the way for the highly efficient and sustainable development of new-generation self-powered visualized perception systems, contributing a solution to wireless communication free from electromagnetic interference.

Advanced Applications of Porous Materials in Triboelectric Nanogenerator Self-Powered Sensors

Porous materials possess advantages such as rich pore structures, a large surface area, low relative density, high specific strength, and good breathability. They have broad prospects in the development of a high-performance Triboelectric Nanogenerator (TENG) and self-powered sensing fields. This paper elaborates on the structural forms and construction methods of porous materials in existing TENG, including aerogels, foam sponges, electrospinning, 3D printing, and fabric structures. The research progress of porous materials in improving TENG performance is systematically summarized, with a focus on discussing design strategies of porous structures to enhance the TENG mechanical performance, frictional electrical performance, and environmental tolerance. The current applications of porous-material-based TENG in self-powered sensing such as pressure sensing, health monitoring, and human-machine interactions are introduced, and future development directions and challenges are discussed.

Underwater Gesture Recognition Meta-Gloves for Marine Immersive Communication

Rapid advancements in immersive communications and artificial intelligence have created a pressing demand for high-performance tactile sensing gloves capable of delivering high sensitivity and a wide sensing range. Unfortunately, existing tactile sensing gloves fall short in terms of user comfort and are ill-suited for underwater applications. To address these limitations, we propose a flexible hand gesture recognition glove (GRG) that contains high-performance micropillar tactile sensors (MPTSs) inspired by the flexible tube foot of a starfish. The as-prepared flexible sensors offer a wide working range (5 Pa to 450 kPa), superfast response time (23 ms), reliable repeatability (∼10000 cycles), and a low limit of detection. Furthermore, these MPTSs are waterproof, which makes them well-suited for underwater applications. By integrating the high-performance MPTSs with a machine learning algorithm, the proposed GRG system achieves intelligent recognition of 16 hand gestures under water, which significantly extends real-time and effective communication capabilities for divers. The GRG system holds tremendous potential for a wide range of applications in the field of underwater communications.

Performance-Enhanced Flexible Self-Powered Tactile Sensor Arrays Based on Lotus Root-Derived Porous Carbon for Real-Time Human-Machine Interaction of the Robotic Snake

Flexible tactile sensors play an important role in the development of wearable electronics and human-machine interaction (HMI) systems. However, poor sensing abilities, an indispensable external energy supply, and limited material selection have significantly constrained their advancement. Herein, a self-powered flexible triboelectric sensor (TES) is proposed by integrating lotus-root-derived porous carbon (PC) into polydimethylsiloxane (PDMS). Owing to the superior charge capturing capability of PC, the PDMS/PC (PPC)-based TES exhibits an open-circuit voltage (Voc) of 22.8 V when it is periodically patted by skin at the pressure of 2 N and the frequency of 1 Hz, which is 5 times higher than that of a pristine PDMS-based TES. Furthermore, the as-prepared self-powered TES exhibits a high sensitivity of 3.24 V kPa-1 below 15 kPa for detecting human motion signals, such as finger clicks, joint bends, etc. Last but not the least, after the assembly of a PPC-based TES array and construction of an HMI system, the robotic snake can be controlled remotely by recognizing finger touching signals. This work shows broad potential applications for the self-powered TES in the fields of intelligent robotics, flexible electronics, disaster relief, and intelligence spying.

Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses

The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented interest in the intelligentialize of human society. As an essential component that bridges the physical world and digital signals, flexible sensors are evolving from a single sensing element to a smarter system, which is capable of highly efficient acquisition, analysis, and even perception of vast, multifaceted data. While challenging from a manual perspective, the development of intelligent flexible sensing has been remarkably facilitated owing to the rapid advances of brain-inspired AI innovations from both the algorithm (machine learning) and the framework (artificial synapses) level. This review presents the recent progress of the emerging AI-driven, intelligent flexible sensing systems. The basic concept of machine learning and artificial synapses are introduced. The new enabling features induced by the fusion of AI and flexible sensing are comprehensively reviewed, which significantly advances the applications such as flexible sensory systems, soft/humanoid robotics, and human activity monitoring. As two of the most profound innovations in the twenty-first century, the deep incorporation of flexible sensing and AI technology holds tremendous potential for creating a smarter world for human beings.

Miniaturized Flexible Non-Contact Interface Based on Heat Shrinkage Technology

High-performance miniaturized flexible sensors are becoming increasingly important in wearable electronics. However, miniaturization of devices often requires high-precision manufacturing processes and equipment, which limits the commercialization of flexible sensors. Therefore, revolutionary technologies for manufacturing miniaturized flexible sensors are highly desired. In this work, a new method for manufacturing miniaturized flexible humidity sensor by utilizing heat shrinkage technology is presented. This method successfully achieves much smaller sensor and denser interdigital electrode. Utilizing this method, a miniaturized flexible humidity sensor and array are presented, fabricated by anchoring nano-Al2 O3 into carbon nano-tube as the humidity sensitive film. This heat shrinkage technology, forming wrinkle structure on the humidity sensitive film, endows the sensor with a high sensitivity over 200% (ΔR/R0 ) at humidity levels ranging from 0 to 90%RH and a fast recovery time (0.5 s). The sensor allows non-contact monitoring human respiration and alerting in case of an asthma attack and the sensor array can be adaptively attached to the wrist as a non-contact human-machine interface to control the mechanical hand or computer. This work provides a general and effective heat shrinkage technology for the development of smaller and more efficient flexible circuits and sensor devices.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

Regular exercise paves the way to a healthy life. However, conventional sports events are susceptible to weather conditions. Current motion sensors for home-based sports are mainly limited by operation power consumption, single-direction sensitivity, or inferior data analysis. Herein, by leveraging the 3-dimensional printing technique and triboelectric effect, a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory. By integrating with a belt, this sensor could be used to identify some low degree of freedom motions, e.g., waist or gait motion, with a high accuracy of 93.8%. Furthermore, when wearing the sensor at the ankle position, signals generated from shank motions that contain more abundant information could also be effectively collected. By means of a deep learning algorithm, the kicking direction and force could be precisely differentiated with an accuracy of 97.5%. Toward practical application, a virtual reality-enabled fitness game and a shooting game were successfully demonstrated. This work is believed to open up new insights for the development of future household sports or rehabilitation.

Persistent triboelectrification-induced electroluminescence for self-powered all-optical wireless user identification and multi-mode anti-counterfeiting

Persistent triboelectrification-induced electroluminescence (TIEL) is highly desirable to break the constraints in the transient-emitting behavior of existing TIEL technologies as it addresses the hindrance caused by incomplete information in optical communication. In this work, a novel self-powered persistent TIEL material (SP-PTM) has been created for the first time, by incorporating the long-afterglow phosphors SrAl₂O₄:Eu²⁺, Dy³⁺ (SAOED) in the material design. It was found that the blue-green transient TIEL derived from ZnS:Cu, Al serves as a reliable excitation source to trigger the persistent photoluminescence (PL) of SAOED. Notably, the aligned dipole moment formed along the vertical direction in the bottom ferroelectric ceramics layer acts as an "optical antenna" to promote variation in the electric field of the upper luminescent layer. Accordingly, the SP-PTM exhibits intense and persistent TIEL for about 10 s in the absence of a continuous power supply. Due to such unique TIEL afterglow behavior, the SP-PTM is applicable in many fields, such as user identification and multi-mode anti-counterfeiting. The SP-PTM proposed in this work not only represents a breakthrough in TIEL materials due to its recording capability and versatile responsivity but also contributes a new strategy to the development of high-performance mechanical-light energy-conversion systems, which may inspire various functional applications.