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Cited by 414

The DOI matches its Crossref record; 235 of the 235 references it cites were found in a registry or an index.

Metal‐Organic Frameworks for Photocatalytic Water Splitting and CO2 Reduction

Sun, Kang, Qian, Yunyang, Jiang, Hai‐Long·2023·Angewandte Chemie International Edition

Abstract

Photocatalytic water splitting and carbon dioxide (CO 2) reduction provide promising solutions to global energy and environmental issues. In recent years, metal‐organic frameworks (MOFs), a class of crystalline porous solids featuring well‐defined and tailorable structures as well as high surface areas, have captured great interest toward photocatalytic water splitting and CO 2 reduction. In this review, the semiconductor‐like behavior of MOFs is first discussed. We then summarize the recent advances in photocatalytic water splitting and CO 2 reduction over MOF‐based materials and focus on the unique advantage of MOFs for clarifying the structure‐property relationship in photocatalysis. In addition, some representative characterization techniques have been presented to unveil the photocatalytic kinetics and reaction intermediates in MOF‐based systems. Finally, the challenges, and perspectives for future directions are proposed.

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Metadata

Authors
Sun, K., Qian, Y., & Jiang, H.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2023-02-06
Volume
62
Issue
15
Pages
e202217565-e202217565
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W4317778293
Cited by
414
References
235
FWCI
19.38
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal‐Organic Frameworks for Photocatalytic Water Splitting and CO2 Reduction. https://app.cochat.ai/papers/10.1002/anie.202217565
The paper
Sun, K., Qian, Y., & Jiang, H. (2023). Metal‐Organic Frameworks for Photocatalytic Water Splitting and CO2 Reduction. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.202217565

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.