Intermediate nanofibrous charge trapping layer-based wearable triboelectric self-powered sensor for human activity recognition and user identification

Abstract

Significant research has been conducted to find practical methods to increase the triboelectric charge density of friction surfaces and improve the TENG output performance. In this study, a double-layer nanofibrous-TENG is newly proposed, consisting of MXene/P(VDF-TrFE) as a charge-generating layer and Siloxene/cobalt-nanoporous carbon/P(VDF-TrFE) as a charge-trapping layer, fabricated via a facile electrospinning process. The charge-generating layer generates abundant surface charges owing to the high electronegativity and electron affinity of MXene. Similarly, Siloxene as a filler in the charge-trapping layer improves the dielectric property of the layer, whereas hierarchically porous structure with a large surface area of cobalt nanoporous carbon provides more active sites for charge storage. After utilizing the charge-trapping layer, the current density and surface potential of the double-layer nanofibrous TENG is two-fold higher than the single-layer nanofibrous TENG. Furthermore, the TENG with Nylon 6/6 nanofiber as a positive friction layer, delivers a power density of 19 W m-2, which shows superior output performance compared to the state-of-the-art works. Finally, the fabricated device is attached to the shoe insole, and the triboelectric sensor data is analyzed using cutting-edge deep learning technology, which exhibited an accuracy of 99% in user identification and user activity recognition. Thus, this study investigates the possibilities of using the electrospun double-layer nanofibrous mat to boost the TENG output performance and explores its applications in artificial intelligence and human activity recognition systems.