Kinetic Modeling\nin Temperature-Modulated Semiconductor\nGas Sensor Utilizing Eley–Rideal Mechanism and Its Application\nin Discriminative Detection of VOCs

This study presents a comprehensive kinetic investigation\nfor describing\nthe dynamic sensing process of semiconductor metal oxide (SMO)-based\ngas sensors, with a focus on the Eley–Rideal mechanism as a\nvalid pathway. The modeling elucidates the direct interactions between\nvolatile organic compounds (VOCs) and adsorbed oxygen on the material\nsurface, providing insights into the temperature-dependent response\ncharacteristics of the sensor and addressing selectivity toward different\nVOCs fundamentally. By incorporating the effects of thermal modulation,\nthe kinetic model was validated through theoretical fitting of experimental\ndata obtained from a fabricated SnO<sub>2</sub> nanoparticle gas sensor\nexposed to ethanol, n-propanol, toluene, and butanone at systematically\nvarying operating temperatures. The results demonstrated that the\nmodel accurately captures response transient values, enabling a general\nframework for qualitatively discriminating specific gases based on\ntheir unique reaction kinetics. Furthermore, a machine learning procedure\ncontaining the above model and power laws of quantitative concentrations\nafter identifying compounds was developed for the prediction of target\nVOCs. The qualitative accuracy was determined to be 99.4%, while quantifying\nwith the mean absolute errors of 5.0, 7.5, 4.0, and 8.9 within the\nrange of 25–200 ppm, respectively. The precise and straightforward\nstrategy facilitates the response modeling of SMO-based gas sensors,\noffering a valuable platform for the designing model-driven algorithms\napplicable in gas analysis and monitoring.