FeドープMoS₂単層膜のCO、H₂S、NO₂検出：先進的環境センサーに向けて

In this study, density functional theory (DFT) calculations integrated with machine learning via the crystal graph convolutional neural network (CGCNN) were employed to systematically investigate the electronic, magnetic, thermomechanical, and optical properties of Fe-doped MoS₂ (Fe-MoS₂) monolayers before and after the adsorption of CO, H₂S, and NO₂ gas molecules. The computational results reveal that gas adsorption, particularly for CO and NO₂, induces strong interactions with the Fe-MoS₂ surface, leading to pronounced modifications in the electronic band structure, density of states, and charge redistribution. The thermomechanical and optical responses of the system are found to be sensitive to the nature of the adsorbed gas species, with notable variations observed in the elastic moduli and optical absorption coefficients. The Fe-MoS₂ monolayers exhibit strong optical absorption in the ultraviolet to visible spectral range (200-500 nm), alongside tunable electromagnetic characteristics modulated by gas adsorption. These findings highlight the potential of Fe-MoS₂ monolayers as a multifunctional material for applications in gas sensing and optoelectronic devices.