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Charge-regulated wood-based aerogel triboelectric nanogenerator for sustainable mechanical energy harvesting and self-powered sensing.

Charge-regulated wood-based aerogel triboelectric nanogenerator for sustainable mechanical energy harvesting and self-powered sensing.

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Tianjin, CN · Author affiliation

School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China.
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Beijing, CN · Author affiliation

Beijing Products Quality Supervision and Inspection Institute, Key Laboratory of Furniture Health and Intelligent Quality Safety, State Administration for Market Regulation, Beijing 101300, China.
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Wuhan, CN · Author affiliation

School of Science, Hubei University of Technology, Wuhan 430068, China.
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Original abstract

Triboelectric nanogenerators (TENGs) have attracted increasing attention as promising technologies for sustainable mechanical energy harvesting and self-powered sensing. However, the electrical output of wood-aerogel-based TENGs is still limited by insufficient charge generation and severe charge dissipation during continuous operation. Herein, we report a high-performance TENG based on a carbon-nanotube-modified electrified wood aerogel (CNT/WA), in which the output performance is synergistically optimized through coupled regulation of porous structure and charge transport-dissipation behavior. The layered porous WA skeleton with a rough surface morphology provides excellent mechanical compliance and a large effective contact area for triboelectrification. Meanwhile, CNTs embedded in the scaffold construct a microcapacitor-rich composite dielectric network, which enhances dielectric polarization, promotes interfacial charge generation, and facilitates charge transport into the dielectric bulk. Furthermore, a transport-blocking architecture composed of a thermoplastic polyurethane (TPU) charge-transport layer and a polyimide (PI) charge-blocking layer is introduced to decouple charge transport from charge retention, thereby effectively suppressing charge leakage and lateral diffusion. As a result, the charge density is increased by approximately 35%, and the peak power density reaches 5.97 W m-2, together with excellent operational stability and durability. The resulting TPU-CNT/WA-PI-TENG (TCWP-TENG) demonstrates strong potential for efficient mechanical energy harvesting and stable self-powered sensing. In addition, when integrated with deep learning, the device enables accurate handwriting, gait recognition, Parkinson's disease-related motion assessment and assisted rehabilitation training. This work provides a promising strategy for developing high-performance wood-based triboelectric systems for sustainable energy harvesting and self-powered wearable electronics in intelligent healthcare.

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