Wenxi Guo

Biomechanical energy is abundant but irregular and low‐grade, posing a significant challenge for wearable electronics, where existing energy‐harvesting solutions are mainly limited to charging capacitors via physical storage, falling short of achieving a full chemical‐charging cycle for practical batteries. To bridge this gap, we developed a closed‐loop strategy that integrates an optimized triboelectric nanogenerator, a dedicated lithium‐ion battery charge‐management system, and fiber lithium‐ion batteries (FLIBs). This system achieved a 22.7‐fold increase in the root‐mean‐square current and an 800‐fold reduction in the optimal load resistance. Consequently, the charging rate is increased by a factor of 38.8, allowing, for the first time, the full electrochemical charging cycle of 20 and 50 cm FLIBs to be completed within 2.37 ± 0.10 and 6.06 ± 0.11 h, respectively. The self‐powered backpack system demonstrated practical viability by powering real‐time positioning and outdoor data transmission, marking a decisive advance from energy harvesting to full energy autonomy for wearables.

Protein hydrogels are highly valued for their biocompatibility and biodegradability, yet their practical use has been limited by poor mechanical strength and dehydration. Polymer‒water interactions govern hydrogel mechanics, whereas dehydration causes failure, rendering water retention and hydration-mediated mechanics critical research focuses. Here, we introduce an ion-mediated hydration engineering (IMHE) strategy that reworks the aqueous states within silk fibroin hydrogels via controlled dehydration‒rehydration. This process converts free water into bound water, which enhances chain mobility and stabilizes hierarchical structures. The resulting hydrogel (HSF-MIN) exhibits distinct properties: a 186-fold increase in compressive strength (5.6 MPa), nearly 100% compressible deformation, superelasticity, and long-term stability (maintaining ~80% hydration over 1 year) while maintaining low hysteresis (<5%). These enhancements enable long-term high-performance sensing using low-hysteresis pressure sensors and high-fidelity bioelectric acquisition. We further introduce a water‒polymer interaction model to elucidate the strength‒elasticity synergy. Notably, IMHE also has universality in gelatin hydrogels, thus providing a way for the preparation of high-performance hydrogels.

This paper presents a bio-inspired liquid metal strain sensor achieving high sensitivity and wide sensing range through a localized flow-limiting design. By integrating rigid PDMS inserts into a soft Ecoflex matrix, tensile strain induces local microchannel constriction, producing exponential resistance changes. The sensor delivers a gauge factor (GF) of 0.343 at 0–50% strain (36-fold enhancement) and 296 at 130–150% strain (15,000-fold enhancement), along with a pressure sensitivity of 19.1 MPa–1 (922-fold enhancement). It maintains excellent stability with only 1.5% resistance degradation after 300 cycles. Through pre-stretching to access the high-sensitivity regime, the sensor successfully demonstrates voice recognition, pulse waveform analysis capable of distinguishing "Cun-Guan-Chi" pulse positions, and gesture-based robotic control. This work provides an effective strategy for developing high-performance flexible sensors for healthcare monitoring and human–machine interaction applications.

Harvesting breeze wind (3–5 m/s) energy remains a key challenge for sustainably powering distributed sensor networks. Although triboelectric nanogenerators (TENGs) operate efficiently at low frequencies, their inherently low (RMS) current limits practical energy storage integration. Here, we report a breeze‐driven TENG system coupled with a universal power management strategy that enables, for the first time, full‐cycle charging of lithium‐ion batteries from low‐speed wind. Through structural and electrical optimization followed by dedicated power management, the system achieves a 17.9‐fold enhancement in RMS current density, reaching 64.67 ± 4.6 mA·m ⁻² ·(m·s ⁻¹ ) ⁻¹ under a breeze wind of 3 m/s. This output supports efficient full charging of 30 mAh and 50 mAh lithium‐ion batteries within 6.26 ± 0.19 and 10.37 ± 0.17 h, respectively. We further demonstrate the system's capability by continuously powering a wireless soil defect (landslides and collapses) and environmental monitoring node over a 0.7 km range. Our work provides a scalable and maintenance‐free platform for distributed sensing using gentle wind energy.

The advancement of flexible transparent electrodes (FTEs) faces the challenge of simultaneously enhancing electrical conductivity, optical transparency, and chemical/mechanical stability. This study innovatively employs a thin‐film crackle self‐templating method combined with vacuum‐deposited Cu seed layers to fabricate large area demonstrable, high‐performance Cu transparent mesh films. Electrodeposition is utilized to precisely repair disconnected conductive networks. Concurrently, an in situ sulfidation technique is implemented, forming a dense protective layer of CuS nanosheets on the Cu mesh surface. This endows the film with exceptional chemical stability, exhibiting no performance degradation under harsh conditions, including various types of strong acids/alkalis (pH 1–13), high‐temperature humidity (85 °C/85% RH, duration > 100 h), adhesion test, ultrasonic impact, and 1000 bending cycles (bending radius = 2 mm). The resulting composite film (Cu/CuS micro‐network) achieves a breakthrough ultra‐low sheet resistance of 0.1 Ω/sq at 80%@550 nm transmittance (Figure of Merit = 16 382, haze = 5.52%@550 nm, excluding the effects of the PVA/PVP layer and PET substrate). The fabricated film demonstrates highly efficient electrothermal conversion (>100 °C within 60 s at 1 V, efficiency exceeding 60%). This work provides a large‐area demonstrable, corrosion‐resistant transparent electronic platform promising for applications in wearable electronics, automotive electronics, and smart environments.

Flexible wearable electronics face critical challenges in achieving reliable physiological monitoring, particularly due to the trade-off between sensitivity and durability in flexible electrodes, compounded by mechanical modulus mismatch with biological tissues. To address these limitations, we develop an anti-freezing ionic hydrogel through a chitosan/acrylamide/LiCl system engineered via the solution post-treatment strategy. The optimized hydrogel exhibits exceptional ionic conductivity (24.1 mS/cm at 25 °C) and excellent cryogenic tolerance. Leveraging these attributes, we construct a gel-based triboelectric nanogenerator (G-TENG) that demonstrates ultrahigh sensitivity (1.56 V/kPa) under low pressure. The device enables the precise capture of subtle vibrations at a frequency of 1088 Hz with a signal-to-noise ratio of 16.27 dB and demonstrates operational stability (>16,000 cycles), successfully differentiating complex physiological activities including swallowing, coughing, and phonation. Through machine learning-assisted analysis, the system achieves 96.56% recognition accuracy for five words and demonstrates good signal recognition ability in different ambient sound scenarios. This work provides a paradigm for designing environmentally adaptive wearable sensors through interfacial modulus engineering and ion transport optimization.

A universal energy storage strategy is designed for TENGs in real marine environments, where I _RMS reached 0.86 ± 0.02 A m ⁻² Hz ⁻¹ , enabling the direct charging of 30 and 40 mA h lithium batteries in 2.33 ± 0.05 and 3.73 ± 0.14 h, respectively.

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.