Understanding the interfacial properties of graphene-based materials/BiOI heterostructures by DFT calculations
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Abstract
Abstract Heterostructure constructing is a feasible and powerful strategy to enhance the performance of photocatalysts, because they can be tailored to have desirable photo-electronics properties and couple distinct advantageous of components. As a novel layered photocatalyst, the main drawback of BiOI is the low edge position of the conduction band. To address this problem, it is meaningful to find materials that possess suitable band gap, proper band edge position, and high mobility of carrier to combine with BiOI to form hetertrostructure. In this study, graphene-based materials (including: graphene, graphene oxide, and g-C3N4) were chosen as candidates to achieve this purpose. The charge transfer, interface interaction, and band offsets are focused on and analyzed in detail by DFT calculations. Results indicated that graphene-based materials and BiOI were in contact and formed van der Waals heterostructures. The valence and conduction band edge positions of graphene oxide, g-C3N4 and BiOI changed with the Fermi level and formed the standard type-II heterojunction. In addition, the overall analysis of charge density difference, Mulliken population, and band offsets indicated that the internal electric field is facilitate for the separation of photo-generated electron-hole pairs, which means these heterostructures can enhance the photocatalytic efficiency of BiOI. Thus, BiOI combines with 2D materials to construct heterostructure not only make use of the unique high electron mobility, but also can adjust the position of energy bands and promote the separation of photo-generated carriers, which provide useful hints for the applications in photocatalysis.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Photocatalysis Techniques · Ga2O3 and related materials · ZnO doping and properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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