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Coordination-Modulated MOF-Derived Electrocatalysts for Enhanced C─C Coupling in CO2 to C2H4 and C2H5OH Conversion

  • Ziyun Xi
  • , Mengxia Xu
  • , Hongling Qin
  • , Zijun Yan
  • , Min Liu
  • , Tao Wu
  • , Ning Han
  • , Honglei Zhang
  • , Gang Wu
  • Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute
  • University of Nottingham Ningbo China
  • National Institute of Clean-and-Low-Carbon Energy

Research output: Contribution to journalReview articlepeer-review

Abstract

Electrochemical CO2 reduction (CO2RR) to high-value C2 products- ethylene (C2H4) and ethanol (C2H5OH)- offers a sustainable pathway to mitigate anthropogenic emissions and close the carbon cycle. However, high energy barriers for CO2 activation impede large-scale implementation, necessitating catalysts with exceptional activity, selectivity, and stability. Metal–organic frameworks (MOFs) have emerged as prime candidates by leveraging their tunable architecture, high surface areas, and customizable active sites to enhance CO2 activation and direct C─C coupling for precise product control. This review comprehensively explores mechanistic pathways for C2H4 and C2H5OH formation over MOF-based catalysts through an integrated methodology: elucidating reaction mechanisms of key *CO intermediates during proton-coupled electron transfers; resolving dynamic active sites via in situ spectroscopy; and uncovering energy barriers and reaction pathways through DFT calculation. We further outline strategic research directions, including machine learning-guided MOF screening, heterobimetallic MOFs for synergistic catalysis, and cascade reactors decoupling CO2 activation from C─C coupling. These advances position MOFs as transformative materials for enabling efficient, scalable carbon-neutral CO2RR technologies, while ongoing challenges and development prospects are critically examined.

Original languageEnglish
Article numbere73588
JournalSmall
Volume22
Issue number30
DOIs
StatePublished - May 27 2026

Keywords

  • MOF composites
  • MOF derivatives
  • electrochemical CO reduction
  • machine learning
  • metal-–organic frameworks

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