Understanding the CO2 reduction mechanism over CoCu-based catalyst by combined theoretical and experimental studies
Final Report Abstract
This project dealt with the investigation of CoCu particles for the hydrogenation of CO2 to higher alcohols. This has been a collaborative effort with Tianjin university funded through a combined DFG-NSFC call. We investigated CoCu particles up to several nm in diameter. As these are too large for extensive DFT calculations, we used a machine learning approach that was trained on smaller CoCu particles and applied to calculate larger particles at the accuracy of DFT but with a linear scaling of the computational effort. These calculations revealed that the nanoparticles were core-shell shaped with copper constituting the outer 1-2 monolayers. Using these surfaces, DFT calculations of the reaction mechanism have been performed. Furthermore, we investigated how the presence of adsorbed CO on the surface of the alloy alters its segregation behavior. We found that CO binds strongly to cobalt sides, such that Co is preferentially segregated to the surfaces at high CO pressures. As this results in many different surfaces, with potentially many different adsorbed intermediates, we further developed a simple model that allows to predict surface terminations and coverages based on linear scaling relations between surfaces and adsorbates. We identified a partially segregated surface with 2/3 monolayers of Co and 2/3 monolayers of adsorbed CO as a viable model under realistic reaction conditions. This model has subsequently been used to calculate the reaction mechanism to yield products such as methanol, methane and oxygenates. Both, the successful machine learning approach as well as the surface reaction mechanisms open avenues for future work. This might include to increase the description of the surface processes through the inclusion of multiple active site motifs, multiple adsorbates, and a reliable description of all possible cross-interactions. Due to the vast computational effort associated with this, cheap and accurate methods would be needed. Machine learning might just be such an approach. This will in general advance our understanding of the complexity of surface processes occurring at highly covered and segregated bimetallic alloys employed in heterogeneous catalysis.
Publications
- Adsorption Preference Determines Segregation Direction: A Shortcut to More Realistic Surface Models of Alloy Catalysts. ACS Catal. 2019, 9, 5011
S. Liu, Z.-J. Zhao, C. Yang, S. Zha, K. M. Neyman, F. Studt, J. Gong
(See online at https://doi.org/10.1021/acscatal.9b00499) - Theory-guided design of catalytic materials using scaling relationships and reactivity descriptors. Nature Rev. Mater. 2019, 4, 792
Z.-J. Zhao, S. Liu, S. Zha, D. Cheng, F. Studt, G. Henkelman, J. Gong
(See online at https://doi.org/10.1038/s41578-019-0152-x) - Moderate Surface Segregation Promotes Selective Ethanol Production in CO2 Hydrogenation Reaction over CoCu Catalysts. Angew. Chem. Int. Ed. 2022, 61, e202109027
S. Liu, C. Yang, S. Zha, D. Sharapa, F. Studt, Z.-J. Zhao, J. Gong
(See online at https://doi.org/10.1002/anie.202109027)
