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Computational studies of ionic liquids as co-catalyst for CO 2 electrochemical reduction to produce syngas using COSMO-RS
Transforming carbon dioxide (CO 2 ) into value-added products through electrochemical reduction reaction (CO 2 ERR) is a promising technique due to its potential advantages using renewable energy. The main challenge is to find a stable catalytic system that could minimize the reaction overpotential...
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Published in: | E3S web of conferences 2021, Vol.287, p.2016 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Transforming carbon dioxide (CO
2
) into value-added products through electrochemical reduction reaction (CO
2
ERR) is a promising technique due to its potential advantages using renewable energy. The main challenge is to find a stable catalytic system that could minimize the reaction overpotential with high faradaic efficiency and high current density. Ionic liquids (ILs) as electrolyte in CO
2
ERR have attracted attention due to the advantages of their unique properties in enhancing catalytic efficiency. For better performance, a systematic understanding of the role of ILs as electrocatalyst is needed. Therefore, this paper aims to correlate the performance of ILs as co-catalyst in (CO
2
ERR) with the lowest unoccupied molecular orbital (LUMO) energy level and the interaction energy as predicted by quantum chemical calculation using Conductor like Screening Model for Real Solvents (COSMO-RS) and Turbomole. The results show strong linearity (R
2
=0.98) between hydrogen bond energy (HB) and LUMO values. It is demonstrated that as HB increases, the LUMO value decreases, and the catalytic activity for CO
2
ERR also increases. This result allows further understanding on the correlation between the molecular structure and the catalytic activity for CO
2
ERR. It can serve as a priori prediction to aid in the design of new effective catalysts. |
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ISSN: | 2267-1242 2267-1242 |
DOI: | 10.1051/e3sconf/202128702016 |