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• Comprehensive review of DFT-guided strategies for MOS gas sensor optimization. • Mechanistic insights into gas adsorption on ZnO, TiO 2 , and SnO 2 from DFT studies. • Evaluation of humidity and temperature effects in DFT-based sensing models. • Refinement of metrics for chemisorption–physisorption differentiation. • Future roadmap: AI integration, defect engineering, multi-scale simulations. Metal oxide semiconductor (MOS) gas sensors have garnered significant attention due to their high sensitivity, selectivity, and long-term reliability in detecting harmful gases. Density Functional Theory (DFT) has become a crucial tool for investigating gas adsorption at the atomic level, enabling researchers to optimize material characteristics for improved sensor performance. This review explores both the theoretical foundations and real-world applications of DFT in the context of MOS-based gas sensors, focusing on widely studied materials such as ZnO, TiO 2 , and SnO 2 . It examines how these semiconductors interact with oxidizing gases like O 2 and NO 2 , as well as reducing gases such as H 2 , CO, and NH 3 . The influence of factors like doping, heterostructure formation, and surface modification on sensor selectivity and response is thoroughly analyzed. Despite progress, several challenges remain, including accurately differentiating between physisorption and chemisorption, modeling realistic conditions (e.g., humidity and temperature), and addressing computational limitations. Future research should prioritize integrating DFT insights with experimental data, leveraging machine learning approaches, and refining theoretical models to support practical applications. Overall, this review provides a roadmap for advancing MOS gas sensors toward enhanced capabilities in environmental monitoring, industrial safety, and healthcare diagnostics.
Tangirbergen et al. (Mon,) studied this question.