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Abstract
One of the main metal-oxides that has been widely used in gas sensing and catalysis is SnO2. To better understand the underlying physics behind the sensing mechanism, this study focuses on analyzing oxygen vacancies and their determining factor at working sensor conditions. The highlight of this research is in using a combination of two different computational approaches, Monte Carlo and Density Functional Theory. In brief, a model Hamiltonian is constructed from a set of DFT calculations, after testing and analyzing each parameter, Monte Carlo simulations are performed at a fix given chemical potential of the environment. Investigations on (101) surface not only agrees with earlier studies, but also depicts a finer resolution of vacancy patterns. The same approach was then applied to the most stable surface terminations of SnO2 (110) that shows novel patterns of vacancies not observed before. With the success of the model in predicting vacancies of the pure system, it is later applied to the Pd-doped system, successfully showing the role of transition metals in expediting the formation of oxygen vacancies and consequently, improvement in the sensing toward target gas species. With the calculated results of this study, the model Hamiltonian approach has the potential to be used in wide range of different surface, either pure or doped, in contact with a reservoir.