A flexible, sustainable, and deep learning-assisted triboelectric patch for self-powered interactive sensing and wound healing applications

Ko Yu Hsu, Shih Min Huang, Bayu Tri Murti, Chien Chang Chen, Ying Chin Chao, I. Chun Ha, Chih Chun Tsai, Ching Yun Chen, Meng Lin Tsai, Po Kang Yang

研究成果: 雜誌貢獻期刊論文同行評審

摘要

Multi-functional cellulose-based triboelectric nanogenerators (TENGs) with sensing and energy-harvesting capabilities are emerging as promising candidates for next-generation healthcare electronics. However, insufficient output performance and device sustainability limits their further application. In this study, we developed a SnS₂-based nanocomposite with tunable surface triboelectric properties, simulated by Density Functional Theory (DFT) and characterized via Kelvin Probe Force Microscopy (KPFM). The SnS₂-based nanocomposite was then integrated into a cellulose-based TENG (C-TENG) to enhance output performance and function as a biomechanical sensing medium for human motion monitoring. A one-dimensional geometric fast data density functional transform (1-D g-fDDFT) model was also employed to improve the as-designed sensor prediction accuracy. Moreover, the C-TENG was utilized as a self-powered in vitro electrical stimulation device for wound therapy. The C-TENG not only shows excellent potential for future sustainable, self-powered healthcare sensors, but also represents a promising advancement in future wearable wound management systems.

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文章編號110501
期刊Nano Energy
134
DOIs
出版狀態已出版 - 2月 2025

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