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High-selectivity CO2-to-CH4 electrochemical reduction on copper trimer: A theoretical insight

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

The pursuit of enhanced catalysts represents a pivotal yet challenging undertaking in the realm of CO2 reduction to CH4 powered by renewable electricity. The development of high-performance catalysts has been constrained by the scaling between *CO and *CHO, coupled with inadequate selectivity. Here, we report a design strategy that addresses the limitation by formulating Cu trimer-anchored MXene-based catalysts using density functional theory studies. This advancement was achieved by enhancing the selectivity of *OCHO via constructing oxyphilic sites, introducing a hydrogen bond promoter for *HCOOH deep reduction, and a multi-site synergy strategy for stabilizing complex intermediates (e.g., *H2COOH), and therefore reshaping the linear relationship between adsorbates. Consequently, a novel volcano model was constructed using the adsorption free energy of *OCHO as an activity descriptor. Based on the microkinetic analysis, it is predicted that a high current density could potentially be achieved on Cu3@V2NO2 at an applied potential of −1.10 V vs. reversible hydrogen electrode. This study proposes new catalyst design approaches for CO2 conversion, accompanied by a thorough exploration of the thermodynamic and kinetic aspects of the reduction process. © 2024 Published by Elsevier B.V.
Original languageEnglish
Article number104498
JournalSurfaces and Interfaces
Volume50
Online published17 May 2024
DOIs
Publication statusPublished - Jul 2024

Funding

This work was supported by the Research Grants Council of Hong Kong (CityU 11305919 and 11308620 ) and NSFC/RGC Joint Research Scheme N_CityU104/19. Hong Kong Research Grant Council Collaborative Research Fund: C1002-21G and C1017-22G. This research made use of the computing resources of the X-GPU cluster supported by the Hong Kong Research Grant Council Collaborative Research Fund: C6021-19E

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Research Keywords

  • CO2 reduction
  • Cu3
  • Density functional theory
  • Design strategy

RGC Funding Information

  • RGC-funded

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