Water Vapor‐Enhanced Selective Production of Methane During Photothermal CO2 Reduction: Mechanistic Insights Into Boron‐Doped Nickel Catalysts

Boron‐doped nickel catalysts achieve exceptional photothermal CO2 methanation performance, reaching 195.85 mmol g−1 h−1 CH4 yield with 80.21% selectivity through optimized H2/H2O feeding. Boron doping modulates the Ni d‐band center, strengthening CO adsorption and facilitating subsequent hydrogenation. This work provides atomic‐level insights into photothermal catalytic mechanisms and pathway control for sustainable CO2 conversion. Abstract The development of efficient CO2 reduction technologies is crucial for mitigating climate change and advancing sustainable energy systems. In this study, the photothermal catalytic reduction of CO2 to methane (CH4) using boron‐doped Ni as a catalyst, focuses on enhancing product selectivity through reaction parameter optimization. Notably, addition of water vapor substantially improves both CH4 yield (195.85 mmol g·h−1) and selectivity (80.21%), representing a significant advancement over traditional approaches. Through integrated experimental characterization and density functional theory (DFT) calculations, the underlying mechanism involving competitive adsorption dynamics is elucidated between CO2 and H2O molecules on the boron‐doped Ni surface, along with their parallel dissociation pathways. DFT calculations also confirm that boron doping upshifts the Ni d‐band center, strengthening CO adsorption for subsequent hydrogenation. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements indicated that the reverse water‐gas shift (RWGS) reaction on boron‐doped Ni proceeded primarily via a dissociation pathway. Furthermore, the introduction of H2O enables alternative CO2 reduction mechanisms through the carboxylate and bicarbonate pathways. This study demonstrates the significant potential of boron‐doped Ni catalysts for enhanced CO2 methanation and provides valuable mechanistic insights into how reaction parameters influence the photothermal CO2 reduction process. Boron-doped nickel catalysts achieve exceptional photothermal CO 2 methanation performance, reaching 195.85 mmol g −1 h −1 CH 4 yield with 80.21% selectivity through optimized H 2 /H 2 O feeding. Boron doping modulates the Ni d-band center, strengthening CO adsorption and facilitating subsequent hydrogenation. This work provides atomic-level insights into photothermal catalytic mechanisms and pathway control for sustainable CO 2 conversion. Abstract The development of efficient CO 2 reduction technologies is crucial for mitigating climate change and advancing sustainable energy systems. In this study, the photothermal catalytic reduction of CO 2 to methane (CH 4 ) using boron-doped Ni as a catalyst, focuses on enhancing product selectivity through reaction parameter optimization. Notably, addition of water vapor substantially improves both CH 4 yield (195.85 mmol g·h −1 ) and selectivity (80.21%), representing a significant advancement over traditional approaches. Through integrated experimental characterization and density functional theory (DFT) calculations, the underlying mechanism involving competitive adsorption dynamics is elucidated between CO 2 and H 2 O molecules on the boron-doped Ni surface, along with their parallel dissociation pathways. DFT calculations also confirm that boron doping upshifts the Ni d-band center, strengthening CO adsorption for subsequent hydrogenation. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) measurements indicated that the reverse water-gas shift (RWGS) reaction on boron-doped Ni proceeded primarily via a dissociation pathway. Furthermore, the introduction of H 2 O enables alternative CO 2 reduction mechanisms through the carboxylate and bicarbonate pathways. This study demonstrates the significant potential of boron-doped Ni catalysts for enhanced CO 2 methanation and provides valuable mechanistic insights into how reaction parameters influence the photothermal CO 2 reduction process. Advanced Science, EarlyView.