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

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 MoS₂ 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 MoS₂ 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.