Density Functional Theory Investigation of Atomic Quantum Clusters on Graphene and SnO for Enhanced CO₂ Electrochemical Reduction

Abstract

Fossil fuel combustion is a major source of atmospheric CO₂ and significantly contributes to environmental concerns. To address this issue, effective strategies are required to reduce emissions while simultaneously producing valuable materials, such as converting CO₂ into useful carbon-based chemicals. To systematically investigate the geometric and electronic structures of new catalysts for such conversion, density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package (VASP). The generalized gradient approximation (GGA) was employed to describe the exchange–correlation functional, enabling accurate geometry optimisation and detailed analysis of electronic properties. This thesis focuses on investigating the CO₂ reduction reaction (CO₂RR) using electrocatalysts based on graphene and SnO electrodes. It also examines the properties of pristine and defective graphene doped with Ag₅ and Au₅ atomic quantum clusters (AQCs), as well as the influence of the SnO oxide surface and Bi doping. To develop understanding of these materials, Bader charge analysis, charge density differences, and binding energies of these slabs are presented. The results suggest that incorporating Ag₅ and Au₅ into graphene and Bi into SnO significantly lowers the energy barriers of key intermediates, thereby enhancing selectivity toward products such as carbon monoxide (CO) and formic acid (HCOOH). Accordingly, Ag₅, Au₅, and Bi are proposed as effective surface modifications to optimise the catalytic performance of graphene and SnO for CO₂ reduction. These findings provide insight into surface engineering through defect and doping strategies, contributing to the optimisation of catalyst efficiency. Finally, the thesis concludes with a summary of the main findings and a discussion of future research directions.