Site‐Selective MoS2‐Based Sensor for Detection and Discrimination of Triethylamine from Volatile Amines Using Kinetic Analysis and Machine Learning

Abstract Detection and discrimination of volatile organic compounds (VOCs) is important to provide a more realistic assessment of their potential implication in complex environments and medical diagnostics based on volatile biomarkers. Herein, chemiresistive sensors are fabricated using stacked MoS 2 nanoflakes with defects and exposed‐edge sites. The sensor is found to be extremely selective to triethylamine (TEA) over polar, non‐polar VOCs and atmospheric gases. The sensor exhibits a sensitivity of 1.72% ppm −1 , fast response/recovery (19 s/39 s) to 100 ppm TEA at room temperature, low limit of detection (64 ppb), device reproducibility, humidity tolerance (RH 90%) and stability tested up to 60 days. The kinetic analysis of sensing curves reveals two discrete adsorption sites corresponding to edge and basal sites of interaction, with a higher rate constant of association and dissociation for TEA. The Density Functional Theory (DFT) studies support higher adsorption energy of TEA on MoS 2 surface with respect to other volatile amines. The sensor demonstrates TEA recognition and composition estimation capability in a binary mixture of a similar class of VOCs using Machine Learning driven analysis with 95% accuracy. The ability to discriminate amines in binary mixture of other volatile amines paves the way for the advancement of next‐generation devices in the field of disease diagnosis.