Tongue‐Inspired Dual‐Mode Sensing System for Effective Liquid Identification

Abstract The identification and analysis of liquid substances is critical across environmental safety, chemical detection, and food industry applications, yet existing methods struggle with broad‐spectrum recognition and continuous in situ detection. This study presents a dual‐mode bionic liquid sensor (DBLS) inspired by the human gustatory mechanism, integrating a non‐contact piezoresistive force sensor with a triboelectric nanogenerator (TENG) module featuring four differentiated triboelectric superhydrophobic layers. Unlike conventional droplet based TENG sensors, the motor‐actuated impact mechanism overcomes performance degradation during repeated use and signal attenuation in high ionic concentrations. The DBLS generates comprehensive liquid profiles by combining material‐specific triboelectric responses with force measurements that capture surface tension and viscosity properties. This approach accommodates a wide range of liquid compositions and physical properties while effectively eliminating the influence of variable contact conditions on recognition accuracy. When coupled with machine learning, the system demonstrates exceptional discrimination capabilities, differentiating salt solutions and 14 common beverages with over 99.4% accuracy in identifying contaminants. With remarkable durability (5000 cycles) and versatility, the DBLS provides a promising platform for food safety, environmental monitoring, and industrial quality control applications.