Integrating plasmon and vacancies over oxide perovskite for synergistic CO2 methanation
Shuwen Cheng, Zhehao Sun, Kang Hui Lim, Claudia Li, Martyna Judd, Nicholas J. Cox, Rosalie K. Hocking, Ying Liu, Xuechen Jing, Xiaozhou Liao, Guohua Jia, Sibudjing Kawi, Zongyou Yin
Abstract
The photocatalytic reduction of CO 2 to CH 4 offers a promising path for sustainable energy conversion, but its complexity, requiring an eight-electron transfer, poses significant challenges. This study presents a novel method to enhance the activity and selectivity of this reaction using Ag nanoparticles as cocatalysts on a mesoporous perovskite semiconductor, NiTiO 3 . By leveraging the synergistic effects of localized surface plasmon resonance (LSPR) and strategically engineered vacancies, the Ag-NiTiO3 catalyst achieves a 15-fold increase in CH 4 production and near-perfect selectivity, up from 92.4 % in pristine NiTiO 3 . Advanced simulations, including finite-difference time-domain (FDTD) and density functional theory (DFT), highlight the crucial role of LSPR-induced local electric fields and vacancies in enhancing methane selectivity. The integration of Ag nanoparticles into the NiTiO 3 matrix not only facilitates efficient electron-hole separation but also promotes the formation of vacancies essential for the CO 2 to CH 4 conversion. This work offers profound insights into the interaction between light, plasmonic materials, and semiconductor properties, providing a robust platform for optimizing photocatalytic performance. These findings advance our understanding of photocatalytic CO 2 reduction mechanisms, paving the way for designing more efficient and selective photocatalysts, contributing to broader CO 2 utilization strategies and addressing global carbon emissions and energy challenges. • Plasmon-Enhanced Photocatalysis: Ag-NiTiO 3 catalyzes the reduction of CO 2 via localized surface plasmon resonance effect. • Vacancy Engineering Synergy: Oxygen vacancies significantly boosting catalytic efficiency and methane production. • Mechanistic Insights via Simulations: FDTD and DFT analyses reveal the synergistic of LSPR effect and vacancies. • Exceptional Performance and Scalability: Chemical reduction method achieves higher CH 4 production and selectivity.