Multivariable Sensor Based on Mxene and Machine Learning for Selective Detections of VOCs

Abstract

Two-dimensional transition metal carbides/nitrides, known as MXenes, have recently received significant attention for gas sensing applications. However, Mxenes have strong adsorption to many types of volatile organic compounds (VOCs), and therefore gas sensors based on MXenes generally have poor selectivity. Herein, we developed a Ti3C2TX based multivariable sensor which allows identification of different VOCs, and concentration prediction of the target VOC in variable backgrounds. The sensor’s responses from the broadband impedance spectra create a unique fingerprint for each VOC. Based on the methodologies of principal component analysis and linear discrimination analysis, we demonstrate highly accurate identifications for different types of VOCs and mixtures using this multivariable sensor. Furthermore, we demonstrate an accuracy of 93.2% for the prediction of ethanol concentrations in the presence of different concentrations of water and methanol. The high level of identification and concentration estimation shows the great potential of MXene based multivariable sensor for detection of target VOCs in the presence of known and unknown interferences.