Proceedings of nanoGe Fall Meeting19 (NFM19)
DOI: https://doi.org/10.29363/nanoge.nfm.2019.131
Publication date: 18th July 2019
The direct electrochemical CO2 reduction emerges as an attractive technology for its capability of converting waste CO2 into value-add chemicals and fuels. To achieve a full mechanistic understanding of such electrochemical conversion, vast experimental and computational studies have been implemented, and, aldehydes are found as the reactive intermediates for higher-value hydrocarbons and alcohols. It is worth to notice that, the products spectrum of aldehydes reduction – either to hydrocarbons or alcohols – is contingent on the nature of the catalysts. For instance, aldehydes electrolysis on metallic surface exclusively generates alcohols,[1], [2], [3] whereas the analogous reaction on nonmetallic single-site M–N–C catalysts selectively yields hydrocarbon as the only product.[4], [5] To date, the respective mechanistic routes have not been clearly addressed yet.
In this talk, I present our joint experimental-computational study aiming at understanding the mechanistic details of the hydrocarbons and alcohols formation on typical metal surface and a family of single-site M–N–C catalysts (M = Mn, Fe, Co, Ni). Formaldehyde is used as the single-carbon reactant for this series tests. We monitor the products spectrum of CH2O reduction – methane versus methanol – using chromatograph, and, simulate the atomic reaction steps with density functional theory. It is found that, binding types of the aldehyde, namely, carbon-adsorption or oxygen-adsorption, determines the possibility of its further hydrogenation; while the selectivity, either towards methane or methanol, is controlled by the protonation location. Our finding explains the distinct selectivity- and mechanism- differences of formaldehyde reduction over the metallic and single-site M-N-C catalysts, clarifying one aspect of the complex map of comprehensive electrochemical CO2 reduction.
Wen Ju and Peter Strasser acknowledge financial support by the Cluster of Excellence (“UniSysCat”) funded by the Deutsche Forschungsgemeinschaft and managed by the TU Berlin.
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