Fangmeng Liu

Physical therapies for wound management are attractive because of comfort and noninvasiveness. However, existing devices typically deliver a single therapeutic modality and fail to provide integrated mechanical and electrical regulation at wet, deformable wound interfaces. Here, we report an integrated adhesive-contractile bioelectronic device that combines mechanical contraction, robust tissue adhesion, localized electrical stimulation, and wound status monitoring within a soft and conformal platform. The concentric biomimetic architecture features a thermoresponsive inner ring generating sustained centripetal traction (5.5 kPa) and a surrounding adhesive layer providing robust wet-tissue fixation (148 J/m2) for efficient force transmission without traumatic removal. Deformable serpentine molybdenum electrodes accommodate tissue motion and maintain a stable physiological electric field (100 mV/mm) during healing. This coordinated mechano-electric regulation accelerates wound repair by guiding dermal remodeling, modulating immune responses, and promoting angiogenesis. Meanwhile, the device enables real-time sensing of wound electrical properties, allowing for continuous monitoring of changes during the healing process. Together, this platform offers an integrated strategy for the dynamic regulation of the wound microenvironment in wearable bioelectronic treatment.

In this study, a tunable microwave gas sensor (T-MGS) is proposed and demonstrated. By cascading a pair of varactor diodes at the terminal of the coupled line (CL) resonator and adjusting the direct current (DC) bias voltage, the capacitance of varactor diodes and the surface current distribution of the resonator can be effectively modified, thereby synchronously regulating the equivalent capacitance and inductance of the resonator. This enables significant enhancement of both the electric field (E-field) and magnetic field (H-field) inside the resonator, which effectively compensates for the loaded quality factor (QL) and unloaded quality factor (QU), and further improves the sensing performance of the sensor. To systematically demonstrate the material-agnostic nature of the proposed sensitization strategy, experiments were conducted employing a diverse panel of sensing materials (SMs) spanning the full conductivity range from insulating to highly conductive, and their performance was characterized against reducing NH3 and oxidizing NO2 analytes, representing two fundamentally distinct redox-based sensing mechanisms. Experimental results show that after tuning, the minimum limit of detection (LOD) of the NO2 sensor can reach 0.7 ppb, with a peak sensitivity of 9.779 dB/ppm (1.7 times higher); the minimum LOD of the NH3 sensor can reach 2.3 ppb, with a peak sensitivity of 1.279 dB/ppm (7.6 times higher). Validated in all experimental systems, this sensitization method can significantly improve the sensor sensitivity by 1.7–16 times and greatly reduce the LOD, showing great application potential in the field of high-performance microwave gas sensing.

Reliable gas sensing in dynamic environments is essential for safety, environmental surveillance, and health monitoring. Motion-induced noise easily masks weak signals and compromises the low limit of detection (LOD). Existing circuit- and algorithm-based methods mainly reduce noise during propagation or postprocessing. However, their effectiveness becomes limited when weak signals overlap with nonstationary noise. Herein, we propose a source-level noise reduction strategy based on continuous nanoscale heterointerfacial phonon engineering. The continuous heterointerface promotes interfacial scattering and relaxation of nonequilibrium phonons, thereby reducing their perturbation into charge transport. The MoBTx/ZnO sensor exhibits an ultralow noise of 0.0005%, achieving one order lower than the current methods. It detects 5 parts-per-billion (ppb) NO2 with a high signal-to-noise ratio of 7200. The theoretical LOD is 0.0088 ppb. This strategy embeds intrinsic noise resilience into the sensing mechanism and provides a generalizable nanoscale route toward robust weak-signal detection in resistance-based sensing under dynamic operation.

Nitrogen dioxide (NO2) is a representative air pollutant, and the development of sensors capable of accurate and stable ppb-level detection at room temperature remains highly desirable. Electrical sensors based on organic small molecules have attracted increasing interest due to their structural tunability, mechanical flexibility, facile integration, and suitability for in situ monitoring. However, despite extensive molecular design efforts, the dynamic evolution of nanostructures and the associated structure−sensitivity relationships remain insufficiently understood. Herein, the molecular spatial conformation is systematically regulated by introducing alkyl chains with different lengths into cyano-substituted distyrylbenzene (DSB) derivatives. Meanwhile, microcrystalline formation in the sensing films and the molecular packing lead to exposure of more surface-accessible functional groups, thereby enhancing the NO2 sensitivity. The optimized device achieves the lowest detection limit of 30 ppb. Notably, the humidity-enhanced sensing behavior is attributed to the synergistic interaction between NO2 and H2O. Furthermore, a wireless real-time vehicle exhaust monitoring system based on the flexible sensor demonstrates its promising potential for practical atmospheric environmental monitoring.

Wireless passive optical gas sensors reduce food waste by providing real-time freshness information through continuous biomarker analysis. However, their practical use remains limited by high cost, strict environmental requirements, poor stability, and device rigidity. Here, programmable carbon dots (CDs) were incorporated into a poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) electrospun film to monitor volatile fatty acid (VFA) levels in dairy products via fluorescence color changes. While the rational design of carboxylic and amino groups on the CD surface ensures wide-range pH sensitivity, the hydrophobic polymer matrix enables a self-cleaning mechanism for dynamic sensing. This colorimetric readout is further enhanced by a convolutional neural network model integrated into a mobile app, achieving over 98% accuracy in VFA assessment and providing a precise “best-before date.” The simplicity and integrable design of this VFA sensor make it well suited for incorporation into dairy packaging, where it can reduce food waste and uphold safety standards. Broadly, this dynamic in situ pH sensor with a regenerable surface is effective in both liquid and vapor phases, offering diverse potential practical applications.

Diabetes mellitus (DM) screening remains a major global health concern, with over 60% of cases undiagnosed. While breath analysis offers a noninvasive alternative to blood-based methods, current studies focus solely on acetone and lack diagnostic specificity. Here, we report an integrated diagnostic strategy combining metabolomics-driven biomarker discovery, portable solid electrolyte gas sensors (SEGS), and cellular-level metabolic investigation. Using GC-MS profiling of 130 DM patients and 122 healthy controls, we identified nine discriminative volatile organic compounds (VOCs). A random forest (RF) model achieves a cross-validated AUC of 0.93. The SEGS analyzer detects target VOCs at ppb levels within 30 s, enabling point-of-care (POC) screening. Clinical validation demonstrates 100% accuracy (n = 10) for diabetic ketoacidosis (DKA) detection and 83.3% (n = 30) for DM. Insulin-resistant (IR) cell models uncover breath VOCs’ association with nonvolatile metabolite (NVM) pathways, supporting biological interpretability. This work establishes a biologically interpretable, clinically validated, and field-deployable diagnostic platform for scalable, low-cost DM screening in community and resource-limited settings.

The yttria-stabilized zirconia (YSZ)-based mixed potential gas sensor represents a promising platform for portable acetone detection systems, owing to their high sensitivity and selectivity. However, the practical deployment of these systems is hindered by sensor output drift, circuit noise, and signal fluctuations caused by variations in operating temperature and ambient humidity. To address these challenges, we developed GT-KANet, a novel hybrid deep-learning algorithm designed for robust temperature-humidity compensation and accurate acetone concentration prediction. The GT-KANet architecture integrates Gated Recurrent Unit (GRU) networks for initial temporal feature extraction, transformer layers enhanced with ContraNorm to mitigate oversmoothing during deep feature learning, and a Kolmogorov-Arnold Network (KAN) module for final concentration prediction. Trained on a comprehensive dataset acquired from our independently developed YSZ sensor under controlled conditions, the proposed GT-KANet achieved exceptional predictive accuracy (RMSE = 0.0920 ppm, MAE = 0.0542 ppm) across varying operating temperatures, ambient humidity, and gas concentrations, demonstrating excellent stability and adaptability. Furthermore, by leveraging knowledge distillation, we achieved a 55% reduction in the model's parameter size while maintaining exceptional prediction accuracy, significantly enhancing its feasibility for deployment on resource-constrained embedded platforms. A detailed comparative analysis during development systematically validated the efficacy of each module in boosting prediction accuracy. This work offers a competitive approach for temperature and humidity compensation using YSZ-based acetone sensors, demonstrating excellent application potential in rigorous detection scenarios.

Simultaneous respiration and motion monitoring is essential for self-management of sleep apnea syndrome (SAS), enabling feedback-driven exercise therapy and symptom alleviation. However, current home-based systems lack integrated sensing and require external power, limiting personalized rehabilitation. Here, we develop a dual-functional, self-powered sensor system integrating n-type conducting polymer poly(benzodifurandione) (PBFDO)-based humidity and pressure sensors for concurrent respiration and motion tracking. The humidity sensor employs a localized surface dipole disruption mechanism. Specifically, the weak hydrophilicity of PBFDO confines water interactions to the surface, while its doped π-conjugated skeleton ensures efficient lateral charge transport. Adsorbed water forms local dipoles that scatter carriers, disrupt π-π conduction, and increase resistance. This enables simultaneous achievement of low hysteresis (4.5% RH), high resolution (1%), fast response/recovery (30 s), and a wide linear detection range (11-98% RH), ensuring precise respiratory monitoring. Simultaneously, PBFDO serves as an effective filler in triboelectric self-powered pressure sensors. Its π-π stacking and lamellar ordering enhance interfacial charge transfer and mechanical robustness, achieving a high voltage (160 V), power density (125 mW/m²), and <2.5% output deviation over 2000 cycles. Notably, harvested biomechanical energy directly powers the humidity sensor, enabling continuous and compact operation. This IoT-integrated system offers a scalable self-management solution for long-term SAS rehabilitation.

Wearable triboelectric pressure sensors hold tremendous promise in sign language recognition. Nevertheless, the poor stability and low output are unfavorable to acquire accurate and stable electrical signals for wearable sensors. Herein, we have prepared a waterproof and stable MoBT _x /PDMS‐based triboelectric pressure sensor with a controllable dielectric constant. This pressure sensor illustrates high output performance (144 V, 2800 nA, and 46 nC), high stability (5000 cycles stretching, 5000 cycles bending, and 5000 cycles after washing), and long‐term robustness (60 days). The sensor arrays with 7 channels consisting of step‐type and stripe‐type pressure sensors, can generate distinctive responses corresponding to specific hand gestures, contributing to a higher resolution of sign language letters. Integrated with a microcontroller, this system achieves precise gesture recognition and effective conversion of sign language movements into an audio signal for real‐time communication. This work offers a robust strategy toward reliable, high‐resolution, and real‐time sign language communication, advancing assistive technologies for individuals with disabilities.

Wearable bifunctional sensors demonstrate considerable potential for implementation in early warning systems targeting febrile convulsions. However, significant challenges persist in simultaneously achieving real-time and precise detection of dual sensing modalities with accurate signal discrimination for clinical diagnosis. Here, we have fabricated hollow-microstructure Ti₃C₂T_x/poly(benzodifurandione) composite fibers for synergistic optimization of humidity-pressure sensing performance through rational microstructure engineering of the sensitive layer coupled with the dynamic reconfiguration of conductive pathways. The bifunctional sensor provides high sensitivity (168.72 kPa^-1), a low detection limit (0.67 Pa) for pressure sensing, rapid response/recovery time (3 s/20 s), and low humidity hysteresis (2.18% RH) for humidity sensing. Integrated with a control circuit, a febrile convulsion early alarm system is established for real-time monitoring of muscle activities and breathing rate by sending an early alarm to the mobile terminal when in abnormal conditions. This work develops a new avenue for prompt treatment of intelligent healthcare.