This study focuses on the development of a sustainable and highly sensitive Antheraea pernyi(A.pernyi)silk paper-based electrochemical sensor,utilizing the excellent solvent resistance of silk paper as the substrate and incorporating molybdenum disulfide(MoS2)functional units.The electrochemical sensing principle of this sensor is based on a transpiration-driven electricity generation mechanism.Different solvents interact distinctly with the sensor,resulting in transpiration-driven electrical signals with varying characteristics.Although these electrical signals display complex band-shaped patterns lacking sharp peaks or specific mutation points,they exhibit highly reproducible trends for specific solvent systems.Consequently,this study further employs a feedforward neural network to construct a solvent identification model based on the oak silk fibroin ultrafine paper electrochemical sensor.This model processes and analyzes these complex yet highly repetitive electrical signals.The AI-driven A.pernyi silk paper-based electrochemical sensor not only accurately identifies solvents with highly similar chemical structures and properties,such as methanol,ethanol,isopropanol,and deionized water,but also recognizes chemically similar mixed solvent systems,like varying proportions of ethanol-water mixtures.In 100 identification tests,the accuracy was 100％.The development of this AI-driven A.pernyi silk paper-based electrochemical sensor offers a convenient,cost-effective method for rapid and accurate solvent detection,holding significant implications in fields such as alcohol testing,environmental monitoring,and chemical analysis.
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