To meet the demand for early identification and dynamic assessment of age-related hand function decline, an all-textile integrated piezoelectric sensor based on PVDF-TrFE/MWCNTs composite nanofibers is developed. Using commercial silver fabric electrodes and in-situ electrospun nanofiber membranes, the sensor enables passive, accurate grip strength monitoring. MWCNTs improve the dielectric properties and charge collection efficiency, and increase the piezoelectric beta-phase content from 85.5% to 90.4%. The sensor shows high sensitivity (126.03 mV/N), a wide working range (0.1-6 N), and good stability over 16,000 cycles. Integrated with deep learning, the hand motion monitoring system achieves high-precision motion recognition (R2 = 96.51%). Grip strength, control stability, and finger coordination are quantitatively evaluated on 10 young and 11 elderly subjects. This work provides a wearable strategy for early warning and rehabilitation assessment of hand function decline in the elderly, facilitating the application of flexible electronics in aging-related health management.

Abstract In recent years, iontronic pressure sensors have attracted widespread attention in fields such as wearable devices, health monitoring, and human–machine interaction due to their excellent pressure perception capabilities. However, their further development remains constrained by the inherent trade-off between complex fabrication processes and sensing performance. Compared with high-performance devices that rely on complex micro/nanofabrication, iontronic pressure sensors constructed based on porous sponge skeletons offer greater potential for low-cost fabrication, yet they universally face the critical challenge of ineffective regulation over interfacial contact states. To address this, we developed a high-performance porous iontronic pressure sensor (PIPS) by integrating a facile dip-coating process with an interlayer contact modulation strategy. By introducing and optimizing the spacer layer, the PIPS effectively regulates the initiation and evolution of interfacial contact, thereby improving its overall sensing performance. Benefiting from this, the PIPS exhibits a peak sensitivity of 216.82 kPa–1, a broad working range of 0–500 kPa, and outstanding durability over 10,000 cycles. As a proof of concept, the PIPS enabled cross-scale detection ranging from subtle local human motions to large-scale whole-body movements, and also demonstrated potential in assistive communication. Furthermore, coupled with machine learning algorithms, a soft robotic gripper integrated with the PIPS can precisely recognize eight objects with varying hardness, achieving an accuracy of 98.44%.

Flexible pressure sensors face an irreconcilable trade-off among linearity, sensitivity, and signal stability due to viscoelastic creep. Inspired by the gradient modulus characteristics of human skin, this study employs electrospinning to construct a heterogeneous structure composed of a high-modulus nanofiber network embedded in a low-modulus ionic gel. This structure mimics epidermal rigidity (high-modulus nanofiber layer), dermal viscoelasticity (fiber-gel hybrid), and hypodermal compliance (soft ionic matrix) to synergistically redistribute stress and suppress ion migration. The sensor achieves breakthrough performance: a wide linear range (1 MPa) with near-perfect linearity (R-2 = 0.999) and ultrahigh sensitivity (81.3 kPa(-1)), yielding a record linear sensing factor (LSF, 81,300). Simultaneously, it exhibits ultralow creep (1.76 % signal drift under sustained loading)-96.8 % lower than non-structured iongels-enabled by nanofiber-restricted ion pathways. Theoretical modeling reveals a dynamic compensation mechanism where pressure-induced changes in dielectric properties, contact area expansion, and electric double-layer thinning interact linearly. Laboratory validation demonstrates high-fidelity plantar pressure monitoring during gait cycles and machine learning-based prediction of vertical ground reaction forces with exceptional accuracy (R-2 > 0.95). This work establishes a new design paradigm for high-precision flexible sensing by fundamentally resolving long-standing material limitations.

Flexible kesterite Cu 2 ZnSn (S,Se) 4 (CZTSSe) solar cells on metal foils are attractive for lightweight and conformable photovoltaic applications, yet their efficiencies remain significantly lower than those of rigid devices due to severe foil-induced rear-interface losses during high-temperature chalcogenization. These effects include uncontrolled Mo(S,Se) 2 formation, interfacial instability, and the emergence of blocking back-contact barriers that hinder efficient carrier extraction. Here, we introduce a single-crystal graphene interlayer to engineer the Mo foil/CZTSSe interface. The atomically thin graphene layer decouples absorber growth from the mechanically and chemically non-ideal foil surface, suppressing detrimental interfacial reactions and stabilizing the rear interface. As a result, carrier extraction is enhanced, and the back-contact barrier is reduced toward quasi-ohmic behavior. Flexible CZTSSe solar cells with graphene-engineered interfaces achieve a champion efficiency of 11.6% with simultaneous improvements in V OC , J SC , and fill factor. These results highlight rear-interface stabilization as a key strategy for improving flexible kesterite photovoltaics on metal substrates.

Perovskite/silicon tandem solar cells (PSTSCs) suffer from nonconformal perovskite coverage on industrial-grade micron-textured silicon, leading to efficiency and stability degradation. This study combines photoelectrical simulations and experiments to elucidate the degradation mechanisms and develop mitigation strategies. It reveals that conformal deposition improves optical response and reduces electrical losses. Beyond perovskite morphology regulation, increasing the perovskite subcell current can further alleviate performance degradation. To validate simulation results, we fabricate PSTSCs with varying perovskite morphologies and thicknesses, achieving precise current matching and enhanced performance. Notably, the optimized PSTSCs, featuring thick and conformal perovskites, achieve an efficiency beyond 32% (∼1 cm2) with hysteresis (<1%) and enhanced long-term stability, retaining 98% of their initial efficiency after 430 h of continuous maximum power point tracking. These findings not only advance the understanding of textured PSTSCs but also provide practical insights for enhancing their efficiency and stability, thereby facilitating the commercial development of PSTSCs.

ABSTRACT Textile sensors are pivotal for next‐generation wearables, yet their advancement is hindered by challenges including nonlinear high‐sensitivity response and thermal drift. Here we propose a hierarchical assembly strategy to address these limitations and develop a temperature‐robust yarn sensor (TRYS). To fabricate high‐sensitivity piezoresistive fibers, we assemble modified S‐MXene nanosheets and silver nanowires (AgNWs) into the natural wool substrates, whose intrinsic hierarchical microstructure provides stable electrical contact and homogeneous stress distribution, thereby ensuring high sensitivity and linearity. These functionalized fibers are then twisted into sensing yarns and encapsulated using infrared‐reflective antimony‐doped tin oxide (ATO)‐filled polydimethylsiloxane (PDMS). Our TRYS exhibits great performance, featuring an ultra‐high gauge factor of 1468.43 with a linearity of 99.9%, a remarkable linear gauge factor (LGF) of 367, rapid response/recovery times (40/98 ms) and a subtle strain detection limit of 0.05%. With the protection of the ATO/PDMS layer, the TRYS exhibits excellent thermal robustness, achieving a temperature reduction of 42.8 compared with pure wool fibers at 200. Furthermore, integrated with machine learning, our TRYS enables reliable real‐time physiological monitoring and gesture recognition even under extreme thermal variations, demonstrating its potential for early risk warning in firefighting applications.

Ballistocardiography (BCG)-based unconstrained monitoring techniques have garnered considerable research interest for long-term physiological signal monitoring owing to their exceptional wearability and adaptability across various scenarios. However, during measurement, nonnegligible human body weight can induce considerable deformation or even irreversible damage to the internal architecture of sensors, thereby imposing stringent requirements on structural design. Herein, a highly sensitive, pressure-tolerant, conformal, soft, and comfortable smart fabric is proposed for unconstrained BCG signal monitoring. Through nanoscale surface plasma modification, the smart fabric achieves multistage pressure sensing with high sensitivity (0.692 V kPa-1 within a static pressure range of 1-9 kPa, corresponding to human body weight), a broad frequency response (0.5-10 Hz), and excellent mechanical stability (over 10,000 loading-unloading cycles under 7 kPa static pressure). The physiological signal monitoring system developed based on this sensor enables stable and accurate BCG signal acquisition, achieving up to 96% synchronization with electrocardiography signals, and can be seamlessly integrated into commercial mattresses for unconstrained physiological monitoring. Experimental results demonstrate the applicability of the system for daily life heart-rate assessment, highlighting its potential for unconstrained human health detection.

Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment and wearable devices, but datasets combining both sources are limited due to experimental complexities and signal synchronization challenges. Moreover, wearable-based algorithms often fail to capture deep signal features, hindering early detection and prevention of tibial fatigue injuries. In this study, we simultaneously collected data from laboratory equipment and wearable insole sensors during in-place running by volunteers, creating a dataset named WearLab-Leg. Based on this dataset, we developed a machine learning model integrating Temporal Convolutional Network (TCN) and Transformer modules to estimate vertical ground reaction force (vGRF) and tibia bone force (TBF) using insole pressure signals. Our model’s architecture effectively combines the advantages of local deep feature extraction and global modeling, and further introduces the Weight-MSELoss function to improve peak prediction performance. As a result, the model achieved a normalized root mean square error (NRMSE) of 7.33% for vGRF prediction and 10.64% for TBF prediction. Our dataset and proposed model offer a convenient solution for biomechanical monitoring in athletes and patients, providing reliable data and technical support for early warnings of fatigue-induced injuries.

Conformal deposition of perovskite on fully textured silicon bottom cells using low-cost solution processing remains challenging, limiting the process compatibility and power conversion efficiency (PCE) of perovskite/silicon tandem solar cells. Herein, this challenge through synergetic engineering of the perovskite composition and tunneling recombination junction (TRJ) is addressed. The utilization of wide bandgap perovskite with high cesium content and silicon heterojunction (SHJ) bottom cell with hydrogenated nanocrystalline silicon (nc-Si:H) TRJ is found to enable conformal perovskite on fully textured SHJ bottom cells using solution processing. A remarkable PCE of 33.38% (certified 32.94%) is achieved for the tandem, featuring a record short-circuit current density of 21.21 mA cm-2. The tandem displays excellent stability, retaining 80% of its initial efficiency after 2324 h of operation at maximum power point (AM 1.5G, 25 °C).

Flexible pressure sensors have evolved to provide high sensitivity and broad range, yet maintaining high sensitivity at elevated pressures remains challenging. Traditional approaches, which typically rely on a single strategy such as altering the contact area or the tunneling effect, often struggle to sustain high sensitivity under high pressures. Inspired by the human skin's mechanism of opening ion channels under significant pressure, this study introduces the Graded Micro-conformal Tunneling Interface (GMTI) sensor, in which both microstructures deformation and the gradient tunneling and are brought into play. At low pressures (0-10 kPa), the sensor's output is predominantly influenced by contact area changes, yielding sensitivity up to 6667.21 kPa- 1. As pressure increases, the sensor mimics skin by progressively engaging its gradient tunneling layers and expanding electronic channels, thus maintaining high sensitivity (915.08 kPa- 1) even at elevated pressures (10-100 kPa). Benefiting from that, the GMTI sensor can detect fluctuations as small as 200 g under car weighing 1300 kg with a resolution of 0.15 parts per thousand. Additionally, GMTI-based insoles have been developed to measure dynamic ground reaction forces up to 2000 N during running, achieving a predictive accuracy of up to 99 %. The innovative approach offers a new strategy for pressure sensors, enabling high sensitivity even under high pressure and enhancing their applicability in measurements across an ultra-large dynamic range.

The growing demand for non-invasive, real-time health monitoring has driven the development of graphene-based wearable biosensors for point-of-care (POC) diagnostics. This review explores the surface functionalization of graphene and its critical role in enhancing the performance of wearable biosensors for biomarker detection. Leveraging graphene's exceptional electrical, mechanical, and biocompatible properties, we discuss how surface functionalization-such as covalent and non-covalent functionalization, biomolecular probes, and passivation layers-enable highly sensitive and selective detection of biomarkers in biofluids. We categorize biomarkers based on their physical properties and explore various wearable designs, including patches, contact lenses, microneedles, and textiles, highlighting their integration into POC devices. Furthermore, we examine the challenges and opportunities in translating graphene-based sensors from the lab to real-world applications, emphasizing the importance of biocompatibility and surface functionalization for improved performance. By bridging the gap between material science and biomedical engineering, this review provides a roadmap for the development of next-generation graphene biosensors that could revolutionize personalized medicine and point-of-care diagnostics.

In biomechanical sensing, achieving flexible sensors with a broad detection range, ultrahigh sensitivity, and long-term stability remains a major challenge. Inspired by the gradient-modulus structure of human skin, we fabricated a bioinspired gradient-modulus iontronic sensor (GMIS) by integrating a microstructured ionic gel with a glass fiber-reinforced matrix. This design expanded the sensing range and stability, enabling real-time monitoring of multiple physiological signals. Experimental results demonstrated that GMIS maintained ultrahigh sensitivity (2904 kPa-1) over a wide pressure range (∼3 MPa), effectively doubling that of the uniform counterpart. Glass fiber reinforcement enhanced the matrix hydrogen bonding network, effectively reducing viscoelastic-crew-inducedviscoelastic creep-induced drift from 62.28% in the uniform counterpart to 11.8% under dynamic loading. Moreover, the sensor withstood over 3000 loading cycles at 3 MPa. Combined with a convolutional neural network algorithm, the plantar pressure sensing system achieved a Pearson correlation coefficient exceeding 0.91 between measured and predicted values during walking and running. This work establishes a modulus-gradient design strategy for wearable biomechanical sensors, integrating material innovation with biomechanical analysis for musculoskeletal rehabilitation and health monitoring.

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies.

Flexible mechanical sensors hold significant promise for motion assessment and biomechanical analysis. However, achieving high sensitivity and a wide operating range at low cost remains a major hurdle in pressure sensors. Herein, we propose a novel strategy for constructing a conformal force-sensitive interface on textile fiber structures. Specifically, multi-walled carbon nanotubes (MWCNTs) are deposited onto highly compressible polyester-based velcro textiles (PVT) via a low-cost spraying process. Benefiting from the strong synergy between the spraying technique and the PVT fiber structure, the intrinsic microstructure of PVT is preserved while forming highly interconnected conductive pathways, significantly enhancing the piezoresistive performance. Hence, the as-fabricated sensor demonstrates exceptional sensitivity of 3656.8 kPa-1 (0-100 kPa) and an ultrawide detection range (0-3000 kPa), allowing for precise measurement of subtle pressures generated by breathing and high pressures exerted on human feet. Leveraging the scalability of this fabrication method, we develop a 16 x 16-pixel sensor array for spatial pressure mapping. Additionally, we design a multi-channel sensing insole system that, with the assistance of deep learning, accurately estimates vertical ground reaction forces (vGRF) across varying gait speeds, achieving an accuracy exceeding 98 %. More importantly, it enables continuous monitoring of vGRF variations during outdoor runs on different terrains. This work presents an affordable and scalable method for manufacturing flexible pressure sensors with high sensitivity and broad range, paving the way for applications in health monitoring, sports performance evaluation, and rehabilitation care.

The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision. While flexible pressure-sensing insoles show clinical potential, their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity (R2 > 0.99 up to 1 MPa) in conventional designs. Inspired by the tactile sensing mechanism of human skin, where dermal stratification enables wide-range pressure adaptation and ion-channel-regulated signaling maintains linear electrical responses, we developed a dual-mechanism flexible iontronic pressure sensor (FIPS). This innovative design synergistically combines two bioinspired components: interdigitated fabric microstructures enabling pressure-proportional contact area expansion (∝ P1/3) and iontronic film facilitating self-adaptive ion concentration modulation (∝ P2/3), which together generate a linear capacitance-pressure response (C ∝ P). The FIPS achieves breakthrough performance: 242 kPa−1 sensitivity with 0.997 linearity across 0–1 MPa, yielding a record linear sensing factor (LSF = 242,000). The design is validated across various substrates and ionic materials, demonstrating its versatility. Finally, the FIPS-driven design enables a smart insole demonstrating 1.8

Organic electrochemical transistors (OECTs) have emerged as versatile tools in areas such as bioelectronics, wearable devices, and neuromorphic computing. Their advantages include excellent transconductance, low power requirements, and adaptability to different substrates, making them ideal for diverse applications. The control of threshold voltage (Vth) is important for reducing power consumption and enhancing noise margin in complementary circuits. Furthermore, a wide range of Vth tuning can induce transitions between the depletion mode and accumulation mode, which is particularly helpful for generating spiking and nonlinear dynamics for building artificial spiking neurons. However, only a small category of conjugated polymers can be cycled stably and reversibly between a highly doped state and a dedoped state in aqueous electrolytes. Here, we demonstrate the use of different anions to induce threshold voltage shift from -0.16 V to +0.29 V, while maintaining high transconductance (>7 mS), high ON/OFF ratio (>105) and negligible current degradation for over 10,000 cycles. The dynamic tunability in Vth allows a single OECT to have different operating modes and voltage windows, enabling multifunctional devices. We successfully demonstrate a zero-gate biased voltage amplifier for high-performing electrophysiological signal recording (electrocardiography, electromyography, and electrooculography), complementary inverters with high gain and full rail-to-rail swing, as well as organic artificial spiking neurons that display S-shaped negative differential resistance with oscillation functionality and mimicking human neurobiological functions via integration with tactile and photosensors. Our approach offers a straightforward method to tailor OECTs via anion selection, advancing low-power bioelectronics and neuromorphic systems.

Uncooled long‐wave infrared (LWIR) detectors substantially facilitate the advancement of miniaturized, highly integrated, and lightweight human radiation perception technology. However, constrained by materials and device structures, the progress of conventional LWIR detectors has stagnated in efficiently detecting weak human radiation at room temperature. Herein, a pyroelectric photogating‐based uncooled LWIR detector with a graphene‐Al 2 O 3 ‐LiNbO 3 hybrid structure is proposed for highly sensitive human radiation detection. The device leverages the polarized charge of LiNbO 3 to modulate the conductance carriers in graphene, thereby achieving an amplified photoresponse in the LWIR range of 8 to 11 µm. Notably, at room temperature, the device exhibits a superior responsivity of 48 A W −1 under blackbody radiation, surpassing the performance of previous 2D materials‐based LWIR detectors. Remarkably, it can identify the radiation signals of a human finger, a feat previously considered challenging for devices relying on graphene or other 2D materials. These results reveal the potential of pyroelectric photogating and provide valuable insights for developing high‐sensitivity, uncooled photodetectors.

The ballistocardiogram (BCG) represents a promising unconstrained method for capturing cardiac vibrations, effectively mitigating the discomfort and activity limitations often associated with traditional long-term healthcare monitoring. Herein, we introduce a smart wireless flexible sensing system designed for the unconstrained monitoring of BCG and respiration. The core component of the system is a flexible pressure sensor featuring a gradient spherical crown microstructure design, which ensures high sensitivity to weak dynamic pressure signals even under high static pressure. This sensing capability enables the sensor, attached to the seat, to accurately capture subtle physiological signals from seated individuals. Furthermore, the system holds potential for assisting in the diagnosis of heart rate variability, providing new insights into the application of flexible sensors in the realm of unconstrained human health monitoring.

Resistive flexible strain sensors have attracted widespread attention in the field of wearable bioelectronics due to their simple structure and low cost. In recent years, significant progress has been made in the fields of resistive flexible strain sensors with a wide sensing range and high sensitivity, however, their long-term durability in epidermal sensing applications remains a challenge. Common methods of constructing protective layers often lead to unavoidable interlayer interactions, which adversely affect both hysteresis and stability of the sensor. This paper reports a stretchable strain sensor with a Ravioli Pasta structure (RPS) via dual-electrospinning nanofibers and spraying carbon nanotubes, in which the sensing composites with an island-bridge microcrack structure is embedded within a nanofiber film. This design provides three-dimensional restoring forces to aid the healing of microcracks, minimizing the impact of interlayer interactions between the sensing and protective layers, as well as within the protective layer itself, on the sensor performance. In wearable device applications, the flexible strain sensor maintains fast response speed (24 ms) and excellent repeatability (similar to 12,000 cycles) under 50 % strain, with high sensitivity (GF = 37.38) and low hysteresis (gamma = 3.568 %), and is successfully used for real-time physiological signal monitoring and robotic hand control.

Electronic skin has showcased superior sensing capabilities inspired from human skin. However, most preceding studies focused on the dermis of the skin rather than the epidermis. In particular, the pseudo-porous structural domain of the epidermis increases the skin's tolerance while ensuring its susceptibility to touch. Yet, most endeavors on the porous structures failed to replicate the superior sensing performance of skin-like counterparts in terms of sensitivity and/or detection range. Stimulated by the strategy that the epidermis of the skin absorbs energy while producing ionic conduction to the nerves, this work initiatively introduced an easy-to-produce, and low-cost pressure sensor based on ionic-gel foam, and achieved a high sensitivity (2893 kPa(-1)) within a wide pressure range (up to similar to 1 MPa), which ranked among the best cases thus far. Moreover, the factors affecting the sensor performance were explored while the sensing principles were enriched. Inspiringly, the plantar pressure measurement by harnessing the as-prepared sensor unveiled an ultra-broad detection range (100 Pa-1 MPa), thus delivering a huge application potential in the field of robot and health monitoring.