Self-Powered Liquid TVNG Sensor for Robot Tactile and Gustatory Dual-Mode Sensing System

Tribovoltaic nanogenerator (TVNG) offers self-powered, low impedance, and high direct current transient response, making it highly suitable for robotic sensing. However, the poor performance and lack of intelligent sensing terminals in the solid-solid TVNG of the contact separation mode have limited its advanced applications. We present a self-powered liquid TVNG sensor (LTVNGS) coupling sliding and contact-separation modes, integrated with deep learning for robotic tactile and gustatory dual-mode sensing system. The low-resistance aqueous solution at the metal-semiconductor interface promotes the charge transport efficiency of LTVNG, and the sensitivity has been improved from the nA to the µA level. The working mechanism of LTVNG was revealed, and the principle of enhancing sensitivity in the solid-solid noncontact LTVNGS through the qualitative and quantitative analysis of the GO dispersion solution was explained. Besides, LTVNGs achieve prediction of water droplet height. Furthermore, LTVNG achieves tactile material and gustatory liquid recognition with an average identification accuracy exceeding 98%, assisted by deep learning models through the perception of different materials and liquids. This work provides a scientific framework for multimodal TVNG and its application in intelligent robots, offers an innovative approach for robot liquid analysis, and enhances the ability to identify hazardous liquids and the HMI.