Computational Screening of Doped Graphene Electrodes for Alkaline CO2 Reduction
Verma, A. M., Honkala, K., & Melander, M. M. (2021). Computational Screening of Doped Graphene Electrodes for Alkaline CO2 Reduction. Frontiers in Energy Research, 8, Article 606742. https://doi.org/10.3389/fenrg.2020.606742
Published inFrontiers in Energy Research
© 2021 Verma, Honkala and Melander.
The electrocatalytic CO2 reduction reaction (CO2RR) is considered as one of the most promising approaches to synthesizing carbonaceous fuels and chemicals without utilizing fossil resources. However, current technologies are still in the early phase focusing primarily on identifying optimal electrode materials and reaction conditions. Doped graphene-based materials are among the best CO2RR electrocatalysts and in the present work we have performed a computational screening study to identify suitable graphene catalysts for CO2RR to CO under alkaline conditions. Several types of modified-graphene frameworks doped with metallic and non-metallic elements were considered. After establishing thermodynamically stable electrodes, the electrochemical CO2RR to CO is studied in the alkaline media. Both concerted proton-coupled electron transfer (PCET) and decoupled proton and electron transfer (ETPT) mechanisms were considered by developing and using a generalization of the computational hydrogen electrode approach. It is established that the CO2 electrosorption and associated charge transfer along the ETPT pathway are of utmost importance and significantly impact the electrochemical thermodynamics of CO2RR. Our study suggests an exceptional performance of metal-doped nitrogen-coordinated graphene electrodes, especially 3N-coordinated graphene electrodes. ...
PublisherFrontiers Media SA
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF; Postdoctoral Researcher, AoF
Additional information about fundingMM was supported by the Academy of Finland grants 307853 and 317739. AV and KH were supported by the Academy of Finland grant 317739.
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