JOURNAL ARTICLE
Controlling the Polarity of Metal–Organic Frameworks to Promote Electrochemical CO2 Reduction.
Published In: Angewandte Chemie International Edition, 2025, v. 64, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Chen, Junnan; Wang, Guangming; Dong, Yingjun; Ji, Jiapeng; Li, Linbo; Xue, Ming; Zhang, Xiaolong; Cheng, Hui‐Ming 3 of 3
Abstract
The addition of polar functional groups to porous structures is an effective strategy for increasing the ability of metal–organic frameworks (MOFs) to capture CO2 by enhancing interactions between the dipoles of the polar functional groups and the quadrupoles of CO2. However, the potential of MOFs with polar functional groups to activate CO2 has not been investigated in the context of CO2 electrolysis. In this study, we report a mixed‐ligand strategy to incorporate various functional groups in the MOFs. We found that substituents with strong polarity led to increased catalytic performance of electrochemical CO2 reduction for these polarized MOFs. Both experimental and theoretical evidence indicates that the presence of polar functional groups induces a charge redistribution in the micropores of MOFs. We have shown that higher electron densities of sp2‐carbon atoms in benzimidazolate ligands reduces the energy barrier to generate *COOH, which is simultaneously controlled by the mass transfer of CO2. Our research offers an effective method of disrupting local electron neutrality in the pores of electrocatalysts/supports to activate CO2 under electrochemical conditions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Angewandte Chemie International Edition. 2025/01, Vol. 64, Issue 4, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:1433-7851
- DOI:10.1002/anie.202416367
- Accession Number:183600661
- 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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