Photochemical CO2 conversion on pristine and Mg-doped gallium nitride (GaN): a comprehensive DFT study based on a cluster model approach

The photochemical reduction of carbon dioxide (CO2) into methanol is very appealing since it requires sunlight as the only energy input. However, the development of highly selective and efficient photocatalysts is still very challenging. It has been reported that CO2 can be spontaneously activated on gallium nitride (GaN). Moreover, the photocatalytic activity for CO2 conversion into methanol can be drastically enhanced by incorporating a small amount of Mg dopant. In this work, density functional theory (DFT) based on a cluster model approach has been applied to further explore the photocatalytic activity of bare GaN towards CO2 adsorption and conversion. We extended the investigation of Mg-doping replacing one Ga atom with Mg on three different sites and evaluated the consequent effects on the band gaps and CO2 adsorption energies. Finally, we explore different routes leading to the production of methanol and evaluate the catalytic activity of bare GaN by applying the energetic span model (ESM) in order to identify the rate-determining states which are fundamental for suggesting modifications that can improve the photocatalytic activity of this promising material.

Automated summary

The study investigates the photochemical reduction of carbon dioxide into methanol, finding that the efficiency of this process can be significantly enhanced by doping gallium nitride with magnesium. It also examines the impact of doping on band gaps and CO2 adsorption energies using density functional theory (DFT) and evaluates the catalytic activity of bare GaN with the energetic span model (ESM).