Intermediate nanofibrous charge trapping layer-based wearable triboelectric self-powered sensor for human activity recognition and user identification. (April 2023) - Shrestha, Kumar; Pradhan, Gagan Bahadur; Bhatta, Trilochan; Sharma, Sudeep; Lee, Sanghyun; Song, Hyesu; Jeong, Seonghoon; Park, Jae Y.
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. Graphical Abstract: ga1 Highlights: An electrospun double-layer nanofibrous TENG is developed. The current density and surface potential of the double-layer TENG is two-fold higher than the single-layer TENG. The TENG delivers a power density of 19 W m -2, which is superior compared to the state-of-the-art works. A deep-learning model is utilized to analyze the triboelectric sensors data for human activity recognition and user identification.