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Cited by 505

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Metal‐Organic Frameworks for CO2 Chemical Transformations

He, Hongming, Perman, Jason A., Zhu, Guangshan, Ma, Shengqian·2016·Small

Abstract

Carbon dioxide (CO 2), as the primary greenhouse gas in the atmosphere, triggers a series of environmental and energy related problems in the world. Therefore, there is an urgent need to develop multiple methods to capture and convert CO 2 into useful chemical products, which can significantly improve the environment and promote sustainable development. Over the past several decades, metal‐organic frameworks (MOFs) have shown outstanding heterogeneous catalytic activity due in part to their high internal surface area and chemical functionalities. These properties and the ability to synthesize MOF platforms allow experiments to test structure‐function relationships for transforming CO 2 into useful chemicals. Herein, recent developments are highlighted for MOFs participating as catalysts for the chemical fixation and photochemical reduction of CO 2. Finally, opportunities and challenges facing MOF catalysts are discussed in this ongoing research area.

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Crossref · 10.1002/smll.201602711

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Metadata

Authors
He, H., Perman, J. A., Zhu, G., & Ma, S.
Venue
Small
Publisher
Wiley
Published
2016-10-20
Volume
12
Issue
46
Pages
6309-6324
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2532267890
Cited by
505
References
3
FWCI
27.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal‐Organic Frameworks for CO2 Chemical Transformations. https://app.cochat.ai/papers/10.1002/smll.201602711
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He, H., Perman, J. A., Zhu, G., & Ma, S. (2016). Metal‐Organic Frameworks for CO2 Chemical Transformations. Small. https://doi.org/10.1002/smll.201602711

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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.