Dual Experimental and Computational Approach to Elucidate the Effect of Ga on Cu/Ceo2–Zro2 Catalyst for Co2 Hydrogenation
Type
PreprintKAUST Department
Multiscale Reaction Engineering, KAUST Catalysis Center (KCC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.KAUST Catalysis Center (KCC)
Physical Science and Engineering (PSE) Division
Chemical Engineering Program
Date
2022-07-14Permanent link to this record
http://hdl.handle.net/10754/679782
Metadata
Show full item recordAbstract
Intermetallic Cu–Ga catalysts are potential candidates for activating the selective and stable hydrogenation of carbon dioxide to methanol and dimethyl ether. This work explores the structure–function relationship in specific Cu–Ga/CeO 2 –ZrO 2 catalysts with different Ga loadings. Combining experiments with density functional theory calculations, we find the most well-balanced intermetallic Cu–Ga interphase (structure) and promote specific mechanistic pathways of the reaction (function). The experiments yielded the highest selectivity of the desired products when the Cu and Ga amounts were equal. The experimental work and density functional theory calculations demonstrated that methanol is formed through the carboxyl pathway on the Cu catalyst, while Ga promotes the formate pathway. Consequently, the productivities of both methanol and dimethyl ether are enhanced. The experimental results match well with the theoretical calculations. Comparing our results with other Ga-promoting systems, we also prove that Cu achieves better balance than Ni and Co.Citation
Attada, Y., Velisoju, V. K., Mohamed, H. O., Ramirez, A., & Castano, P. (2022). Dual Experimental and Computational Approach to Elucidate the Effect of Ga on Cu/Ceo2–Zro2 Catalyst for Co2 Hydrogenation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4162690Sponsors
King Abdullah University of Science and Technology (KAUST) provided funding for this work. The authors acknowledge the KAUST Supercomputing Laboratory for providing high-performance computational resources and support from the KAUST Core Labs.Publisher
Elsevier BVAdditional Links
https://www.ssrn.com/abstract=4162690ae974a485f413a2113503eed53cd6c53
10.2139/ssrn.4162690