Computational modeling of SnO2 quantum dots gas sensors for CO detection

Abstract

Tin oxide (SnO₂) is the most commonly utilized gas sensing material due to its unique physical and chemical properties. SnO₂ quantum dots are chosen over bulk tin oxide (SnO₂) due to their nanoscale size and high surface-to-volume ratio. Furthermore, the quantum confinement effect results in a larger bandgap and tunable electrical characteristics. This research presents a modified mathematical model to describe the gas sensing mechanism for reducing gases, such as carbon monoxide (CO) using SnO₂ quantum dots as sensing material. This research considers significant factors that impact sensing performance, such as potential, effective carrier concentration, and trapped charge density. It compares SnO₂ quantum dots at room temperature and bulk SnO₂ at 600 K, providing information on how these variables impact sensor behavior. The resistance of quantum dot films in air and in the presence of target gases is analyzed using binomial expansion to estimate the gas sensor response for both first-order and higher-order terms. The model is further extended to investigate the influence of gas concentration on the sensing film resistance and sensor response. The validity of the modified model is confirmed through comparison with the experimental data available in the literature, demonstrating a close agreement and consistent trends. Statistics affirm the model's reliability through analysis of the T-distribution that results in ±27.8087 margin of error. Cross-sensitivity investigation shows that CO gas has better selectivity than other interfering gases. Key factors such as grain size, depletion layer width, temperature, and doping concentration are examined for their impact on sensor performance. Additionally, this research highlights the gas sensor's response and recovery time, which are critical design parameters for efficient gas sensing devices.