MULTIPHYSICS MODELING AND SIMULATION OF GAS SENSOR FOR NO₂ DETECTION

Amid rising environmental concerns, this study looks into the importance of advanced gas sensing technologies through computational modeling and simulation. Utilizing the capabilities of COMSOL Multiphysics software, an examination is conducted on the performance of an innovative gas sensor featuring SnO₂/reduced graphene oxide (rGO) nanocomposites designed for Nitrogen Dioxide (NO₂) detection. The integration of modules for laminar flow, electric current, heat transfer, and electric species transport facilitates a thorough analysis of the sensor’s behavior under varied operational conditions. Through finite element simulations, the complex interaction between material characteristics, surface morphology, and environmental factors is explored, offering predictive insights into NO₂ sensing mechanisms. Anchored in experimental data, the sensor design serves as the foundation for simulation, enabling virtual exploration of performance improvements and optimization strategies. Validation against experimental results, reported by Zhang et al., ensures the credibility and reliability of the computational model, confirming its usefulness as a predictive tool for sensor development. This approach shows promise in accelerating the advancement of gas sensing technologies, thus aiding in the creation of high-performance sensors with enhanced sensitivity and selectivity for critical environmental monitoring and industrial applications.