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Cited by 295

The DOI matches its Crossref record; 204 of the 204 references it cites were found in a registry or an index.

Graphene-Based Nanomaterials for Catalysis

Hu, Maocong, Yao, Zhenhua, Wang, Xianqin·2017·Industrial & Engineering Chemistry Research

Abstract

Graphene is considered as one of the most promising materials in a wide range of applications because of its outstanding electronic, optical, thermal, and mechanical properties. Given its large specific surface area, two-dimensional structure, facile decoration, and high adsorption capacity, numerous graphene-based nanomaterials with unprecedented characteristics have been designed, prepared, and applied in catalysis. In this article, we first reviewed common synthetic methods to prepare graphene-based catalysts followed by critical comments and possible solutions. We then briefly summarized the characterization techniques that were relevant to catalysis applications and their applications in energy conversion, environmental protection, and several other typical fields. Finally, we discussed the challenges and opportunities for the future development of graphene-based nanomaterials in sustainable catalysis. This review provides key information to the catalysis community to design and fabricate graphene-based novel nanomaterials with great performance.

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Crossref · 10.1021/acs.iecr.6b05048

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Citations

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Metadata

Authors
Hu, M., Yao, Z., & Wang, X.
Venue
Industrial & Engineering Chemistry Research
Published
2017-03-24
Volume
56
Issue
13
Pages
3477-3502
ISSN
  • 0888-5885 (ISSN-L)
  • 1520-5045
OpenAlex
W2595050188
Cited by
295
References
204
FWCI
14.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Graphene-Based Nanomaterials for Catalysis. https://app.cochat.ai/papers/10.1021/acs.iecr.6b05048
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Hu, M., Yao, Z., & Wang, X. (2017). Graphene-Based Nanomaterials for Catalysis. Industrial & Engineering Chemistry Research. https://doi.org/10.1021/acs.iecr.6b05048

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.