For the past several decades, there is growing demand for the development of low-power gas sensing technology for the selective detection of volatile organic compounds (VOCs), important for monitoring safety, pollution, and healthcare. Here we report the selective detection of homologous alcohols and different functional groups containing VOCs using the electrostatically formed nanowire (EFN) sensor without any surface modification of the device. Selectivity toward specific VOC is achieved by training machine-learning based classifiers using the calculated changes in the threshold voltage and the drain-source on current, obtained from systematically controlled biasing of the surrounding gates (junction and back gates) of the field-effect transistors (FET). This work paves the way for a Si complementary metal–oxide–semiconductor (CMOS)-based FET device as an electrostatically selective sensor suitable for mass production and low-power sensing technology.

中文翻译：

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静电形成的纳米线传感器对挥发性有机化合物的静电选择性

在过去的几十年中，对开发用于选择性检测挥发性有机化合物（VOC）的低功率气体传感技术的需求不断增长，这对于监视安全性，污染和医疗保健非常重要。在这里，我们报告了使用静电形成的纳米线（EFN）传感器，无需对设备进行任何表面修饰，即可选择性检测同源醇和含有VOC的不同官能团。通过训练基于机器学习的分类器，使用计算得出的阈值电压和电流源漏电流的变化来实现对特定VOC的选择性，该变化是通过对场效应晶体管的周围栅极（结和背栅极）进行系统控制的偏置而获得的（场效应管）。