DFT Studies on Cun/TiO2 (n=1-7) System for Water Splitting

Abstract

Copper deposited on titania is active in numerous catalytic reactions, incl. water splitting [1]. The activity of the system depends on the size of the copper phase, whether it is present as single atoms, sub-nanometer clusters, or nanoparticles.The aim of the current contribution is to elucidate the relationship between the size of the copper active phase and its electronic and geometric properties by performing Density Functional Theory (DFT) calculations with PBE functional and the def2-TZVP basis set.Theoretical model of the system comprised of a cluster model of anatase phase of titania – (TiO2)34 - with clusters of copper of varying sizes on top of it. The ground-state geometric and electronic structures were elucidated. Next, their redox and photochemical properties were determined: frontier (HOMO, LUMO) orbitals were defined, energy gaps, total and partial density of states were plotted and analysed. The interactions between components of the systems, in particular the influence of the support on the copper electronic structure, were investigated. Next, the reactivity of the metal phase towards typical intermediates observed during water splitting (H2O, OH, O, and H) was studied.The performed calculations demonstrated that the presence of metal determines the energetics and character of the frontier orbitals of the catalyst. The size of copper active phase influences the bond lengths and bond orders with studied reagents.Acknowledgements: This work was supported by the National Science Centre, Poland within Solar-Driven Chemistry project no 2019/01/Y/ST4/00024. This publication is based upon work from COST Action CA 18234 “Computational materials sciences for efficient water splitting with nanocrystals from abundant elements”, supported by COST (European Cooperation in Science and Technology). We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2020/013927.Literature:[1] Cheng Cheng, Wei-Hai Fang, Run Long, and Oleg V. Prezhdo, JACS Au 2021, 1, 5, 550–559