Kinetic Modeling in Temperature-Modulated Semiconductor Gas Sensor Utilizing Eley-Rideal Mechanism and Its Application in Discriminative Detection of VOCs

This study presents a comprehensive kinetic investigation for describing the dynamic sensing process of semiconductor metal oxide (SMO)-based gas sensors, with a focus on the Eley-Rideal mechanism as a valid pathway. The modeling elucidates the direct interactions between volatile organic compounds (VOCs) and adsorbed oxygen on the material surface, providing insights into the temperature-dependent response characteristics of the sensor and addressing selectivity toward different VOCs fundamentally. By incorporating the effects of thermal modulation, the kinetic model was validated through theoretical fitting of experimental data obtained from a fabricated SnO₂ nanoparticle gas sensor exposed to ethanol, n-propanol, toluene, and butanone at systematically varying operating temperatures. The results demonstrated that the model accurately captures response transient values, enabling a general framework for qualitatively discriminating specific gases based on their unique reaction kinetics. Furthermore, a machine learning procedure containing the above model and power laws of quantitative concentrations after identifying compounds was developed for the prediction of target VOCs. The qualitative accuracy was determined to be 99.4%, while quantifying with the mean absolute errors of 5.0, 7.5, 4.0, and 8.9 within the range of 25-200 ppm, respectively. The precise and straightforward strategy facilitates the response modeling of SMO-based gas sensors, offering a valuable platform for the designing model-driven algorithms applicable in gas analysis and monitoring.