Beijing Institute of Nanoenergy and Nanosystems

ABSTRACT Wave energy is a vital component of sustainable ocean resources, yet current energy harvesting technologies struggle with the intrinsic coupling effect of low‐frequency resonance and low‐amplitude viscosity in fluid‐solid interaction, thereby constraining the overall energy harvesting performance. Here, we present an amplitude‑amplified triboelectric nanogenerator (AA‑TENG) enabled by resonance‐enhanced fluid‐structure coupling to address these bottlenecks. AA‑TENG employs an elastic prestress modulation to deliver a transient mechanical release mechanism that increases the amplitude by up to 300%, achieving an energy conversion efficiency of 51.2%. A unit outputs a peak power of 5.2 mW at a wave frequency of 1 Hz, while scaling the volume fivefold raises the peak power to 52.48 mW, sufficient to power low‑power marine monitoring devices. This work introduces a wave resonance‐enhanced energy harvesting strategy, promising research into sustainable energy harvesting in the fluid‐structure coupling, advancing the self‐constructed marine microgrids.

ABSTRACT Stretchable hybrid electronics offer a compelling pathway for integrating rigid microelectronic components within soft, deformable platforms, crucial for the development of next‐generation wearable and epidermal devices. However, the pronounced mechanical mismatch at the interface between stiff components and elastomeric substrates often leads to interfacial failure, including delamination and cracking, under dynamic strain conditions. In this study, we present a mechanically optimized and electrically resilient stretchable hybrid electronic system by co‐designing substrate mechanics and conductive architectures. A photolithographically defined gradient crosslinking technique is utilized to fabricate a modulus‐graded polydimethylsiloxane (PDMS) substrate, exhibiting a spatially tunable elastic modulus ranging from 0.12 to 1.4 MPa. This gradient‐modulus configuration enables effective redistribution of localized strain, thereby alleviating stress concentration and enhancing mechanical durability at heterogeneous interfaces. Concurrently, a silver‐PDMS composite conductor is developed, achieving high conductivity (1 Ω/sq under 50% strain), excellent stretchability, and environmental robustness. Direct‐ink‐writing using an inkjet platform facilitates precise patterning of conductive traces, allowing seamless integration of multifunctional sensor arrays capable of real‐time monitoring of wrist kinematics and complex finger movements. This work underscores the synergistic integration of gradient mechanical engineering and scalable microfabrication strategies, paving the way for robust, high‐fidelity wearable electronics and electronic skin systems.

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems.

The early screening of sarcopenia represents a critical clinical need amid the accelerating global aging population. Current diagnostic methods, relying on bioelectrical impedance analysis (BIA), handgrip strength testing, and other clinical examinations, depend on costly medical equipment and struggle to concurrently assess both muscle mass and strength. Herein, we propose a Wearable Sarcopenia Assessment System (WSAS), which employs an integrated hybrid surface electromyography (sEMG)-piezoelectric strain sensing platform to synchronously capture electrophysiological signals and mechanical deformation signals during muscle contraction in handgrip tests (signal-to-noise ratio: 34.32 dB), and incorporates a CNN-LSTM deep learning framework. This model was trained using nine physiologically relevant features (including root mean square (RMS), mean absolute value (MAV), and integrated EMG (iEMG)) extracted through feature engineering as prior knowledge. Validated in a cohort of 75 elderly participants, the proposed system achieved a screening accuracy of 99.85% with an area under the curve (AUC) of 0.97. Shapley additive explanations (SHAP)-based interpretability analysis further revealed that WSAS captures neuromuscular alterations associated with sarcopenia, including type II-to-type I muscle fiber transition and neuromuscular junction remodeling. These results demonstrate the potential of WSAS as a portable, low-cost, and radiation-free platform for early-stage sarcopenia screening.