MoS2-based charge trapping layer enabled triboelectric nanogenerator with assistance of CNN-GRU model for intelligent perception

Self-powered sensing technology and smart perception technology have broad application prospects in flexible and wearable electronics. In this work, a flexible triboelectric nanogenerator (RMP-TENG) based on a room-temperature vulcanized silicone rubber (RTV)@(Molybdenum disulfide (MoS2)/Polyvinyl chloride (PVC)) functional layer is developed using a layer-by-layer self-assembly and material doping strategy. The RTV@(MoS2/PVC) functional layer is divided into a charge-generating layer (RTV/PVC) and a charge-trapping layer (RTV/MoS2). The synergistic effect of PVC and MoS2 has improved the surface roughness and charge transfer efficiency of RMP-TENG, doubling its output performance to 1055 V and 112 μA. In order to further improve the tactile sensing accuracy of RMP-TENG, a deep learning model based on Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is developed. It predicts the type of contact material based on the features of tactile signals, achieving a prediction accuracy of 93.975%. Additionally, by combining mobile smart terminals, the CNN-GRU model, and RMP-TENG, a wireless access control system based on self-powered tactile sensing and intelligent material recognition is developed. Through the optimization of these experiments and algorithms, RMP-TENG has achieved real-time material recognition capabilities. This demonstrates the broad application prospects of RMP-TENG in wearable energy supply, intelligent sensing, human-computer interaction, and other areas. [Display omitted] •A triboelectric nanogenerator with a MoS2 charge-trapping layer was fabricated using a layer-by-layer self-assembly method.•The synergistic effect of PVC and MoS2 doubled the output performance of the RMP-TENG, reaching 1055 V and 112 μA.•A deep learning model based on CNN-GRU was proposed, enhancing the tactile perception accuracy of the RMP-TENG.•A wireless control system was developed based on the CNN-GRU assisted RMP-TENG.