Insights on the Electrochemical CO2 Reduction Pathway via Ab Initio Analysis of Raman Spectroscopy Signals
Federico Dattila a b, Chao Zhan c, Rodrigo García-Muelas b, Arno Bergmann c, Núria López b, Beatriz Roldan Cuenya c
a Department of Applied Science and Technology (DISAT), Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin,10129, Italy
b Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology (BIST), Av. Països Catalans, 16, 43007 Tarragona, Spain.
c Department of Interface Science, Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
#ModElOp - Modeling Electrochemistry in Operando
Lausanne, Switzerland, 2024 November 12th - 15th
Organizers: Federico Dattila and Kevin Rossi
Oral, Federico Dattila, presentation 333
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.333
Publication date: 28th August 2024

Electrochemical CO2 reduction (eCO2R) is a promising technology to store renewable energy into chemical bonds and close the carbon cycle. However, to date its industrial exploitation is limited to the production of CO and HCOO, whilst CO2 conversion to C2+ chemicals is possible only on the laboratory scale.1 Low C2+ activity and selectivity are due as well to poor understanding of the CO2 reduction pathways beyond C1 products, and consequently of the active sites responsible for multi-carbon products, mainly ethanol, ethylene, and n-propanol.2 Spectroscopy study can significantly enhance this understanding, by allowing the detection of reaction intermediates on the surface, and density functional theory (DFT) can support the assignment of these spectroscopic signals.3

Here, by means of DFT simulations, we unveil reaction mechanisms and active sites to produce ethylene and ethanol during the electrochemical reduction of CO2 by supporting the assignment of Raman spectroscopic signals.4,5 In the first study,4 we assessed CO coverage effects on Cu(100). At high CO surface coverage (> 0.5 ML), average CO binding energy decreases due to CO-CO repulsive interactions and the abundance of weakly adsorbed CO on atop sites. Besides, vibrational analysis shows that the Cu-CO stretching peak increases to the detriment of the C=O rotation peak at high CO coverages, in line with analogous experimental observations within the ethylene selectivity window. By employing the Hammer’s decomposition scheme, we confirmed that surface atop CO intermediates are the most reactive species to form the CO-CO dimer, having a very endothermic rebond energy (i.e. proxy of dimer dissociation). In the second work,5 we focused on the selectivity switch between ethanol and ethylene occurring during eCO2R at around –0.8 V vs RHE on oxide-derive copper nanocubes. At this applied potential, four vibrational fingerprints are detected via surface-enhanced Raman spectroscopy (SERS), namely 1182, 1318, 1453, and 1595 cm–1. Density functional theory simulations and further reduction studies of selective precursors attribute these vibrations to the *OCHCH2 intermediate, the first selective precursor to ethanol and n-propanol. The formation of this intermediate is favored by distorted Cu active sites with low s-band states, which stabilize the terminal oxygen.

Based on the insights here proposed, we call for an updated reaction mechanism for CO2 reduction to ethylene and ethanol. Both these products require high CO coverage and undercoordinated sites, which facilitates the CO-CO dimerization step, while distorted Cu atoms stabilize *OCHCH2, opening the selective route to ethanol and n-propanol. These guidelines support the rational design of electrocatalysts to maximize ethanol and ethylene selectivity independently.

The authors thank the financial support from the Spanish Ministry of Science and Innovation (Grants PID2021-122516OB-I00 and RTI2018-101394-B-I00, Severo Ochoa CEX2019-000925-S) and the European Union (projects FlowPhotoChem 862453-FLOWPHOTOCHEM, ELCoREL 722614-ELCOREL, SuPERCO2 101104004-SUPERCO2). The Barcelona Supercomputing Center (BSC-RES) and the ioChem-BD database are further acknowledged for having provided generous computational resources and continuous access to generated datasets.

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