Electrostatic Selectivity of Volatile Organic Compounds Using Electrostatically Formed Nanowire Sensor

Abstract

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.

Keywords: electrostatic selectivity; electrostatically formed nanowire sensor; field-effect transistors; machine learning classifiers; selective detection; threshold voltage; transistor parameters; volatile organic compounds.