色散(光学)
镓
材料科学
模具
还原(数学)
硫化物
光电子学
光学
复合材料
冶金
物理
几何学
数学
作者
Ashraf Abdelrahman Assadig Elameen,Debasis Dutta,S. Duman,Marcin Rosmus,Gianluca D’Olimpio,Bekir Gürbulak,Danil Bukhvalov,Harsh Varshney,Antonio Politano
标识
DOI:10.1021/acs.jpcc.4c08588
摘要
Metal monochalcogenides (MXs) have attracted significant interest due to their unique electronic properties, which can be tuned by varying the thickness. Gallium sulfide (GaS) stands out among MX compounds for its potential in photocatalysis, thanks to its bandgap within the visible range. However, the theoretical predictions of its band structure have not been experimentally validated until now. To bridge this gap, we performed angle-resolved photoemission spectroscopy (ARPES) measurements on bulk GaS to investigate its electronic band structure which revealed that the VBM is located at the Γ point, and from the analysis of isoenergy contours just below the Fermi level, the contours are relatively circular and centered around the Γ point indicating a high degree of isotropy and symmetry in the electronic states. Additionally, density functional theory (DFT) calculations revealed that the valence bands are composed of Ga 4s, Ga 4p, and S 3p orbitals, while the deeper bands are from S 3s orbitals. Furthermore, the theoretical calculations are extended to monolayer, two-layer, and three layer to observe the evolution in the band structure. Our results highlight a unique "Pudding Mold" valence band maximum (VBM) at the Γ point, featuring multiple maxima dispersed throughout the Brillouin zone. When the GaS sample is thinned to monolayers, this band transforms into a "Pudding Mold" shape, characterized by significant corrugation at the Γ point. This transformation predicts an increase density of states (DOS), which is highly advantageous for photocatalysis. The higher DOS enhances the absorption and utilization of visible light, which is essential in photocatalytic applications, and also provides more active sites for catalytic reactions.
科研通智能强力驱动
Strongly Powered by AbleSci AI