Adsorption of CO, NO, and SO2 on hexagonal BaO monolayer: Insights from DFT supported by machine learning

Abstract

Two-dimensional metal oxides are promising for gas sensing and optoelectronic applications, yet the adsorption behavior of BaO monolayers remains unexplored. This work investigates the structural, thermomechanical, and optical responses of a hexagonal BaO monolayer upon exposure to CO, NO, and SO₂ gases. Density functional theory (DFT) is used to analyze adsorption mechanisms, charge transfer, and electronic/optical modifications, while a crystal graph convolutional neural network (CGCNN) complements the study by predicting thermomechanical properties of DFT-optimized structures. All gases are found to physisorb with moderate binding energies and localized charge redistribution. Adsorption enhances the bulk modulus and Debye temperature, particularly for SO₂, suggesting improved stiffness and vibrational stability. Significant changes in dielectric function, optical absorption, and joint density of states are also observed, including new features across UV–visible and infrared regions and the loss of in-plane isotropy. These results establish BaO monolayers as potential platforms for broadband sensing and optoelectronics.