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

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.