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Transient Dangling Active Sites of Fe(III)−N−C Single‐Atom Catalyst for Efficient Electrochemical CO2 Reduction Reaction.

  • Published In: Angewandte Chemie International Edition, 2025, v. 64, n. 16. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Qiu, Yun‐Ze; Liu, Xiao‐Meng; Li, Wenying; Li, Jun; Xiao, Hai 3 of 3

Abstract

The Fe single‐atom catalyst (SAC) with an oxidation state of III anchored on the N‐doped carbon substrate (Fe(III)−N−C) delivers superior activity for catalyzing the electrochemical CO2 reduction reaction (eCO2RR) to produce CO, but its mechanism remains contentious and the commonly adopted FeN4‐C model is not a conformant model for Fe(III)−N−C but for Fe(II)−N−C. Herein, employing the grand‐canonical ensemble modeling with the density functional theory method benchmarked against the high‐level wavefunction theory method, we first identify the conformant model for Fe(III)−N−C to be FeN1C3‐C, and we then unveil that the Fe(III)N1C3‐C SAC generates a novel type of dangling active site transiently under working conditions, in which the Fe single‐atom leaves from the anchoring site by breaking all the Fe−C bonds but retains a stable binding to the substrate by the Fe−N bond. Thus, we further elucidate that this flexible dangling active site of Fe(III)−N−C renders a convoluted reaction network with facile CO2 activation, which delivers superior activity for eCO2RR. Our findings provide a novel understanding of the structure–activity relationship for Fe−N−C and concrete insights into the design of highly active SACs. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Angewandte Chemie International Edition. 2025/04, Vol. 64, Issue 16, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:1433-7851
  • DOI:10.1002/anie.202424150
  • Accession Number:184518187
  • Copyright Statement:Copyright of Angewandte Chemie International Edition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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