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Cited by 266

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

A Review on Lattice Defects in Graphene: Types, Generation, Effects and Regulation

Open access
Tian, Wenchao, Li, Wenhua, Yu, Wenbo, Liu, Xiaohan·2017·Micromachines

Abstract

Graphene, having a perfect two-dimensional crystal structure, has many excellent features such as a high specific surface area, and extraordinary electrical, thermal and mechanical properties. However, during the production process, lattice defects will inevitably be produced. Therefore, the performance of graphene with various defects is much lower than its theoretical value. We summarize the major advances of research into graphene defects in engineering in this paper. Firstly, the main types and causes of defects in graphene are introduced. Secondly, the influence of different defects in graphene on the chemical, electronic, magnetic and mechanical properties is discussed. Also, the control methods of graphene defects are reviewed. Finally, we propose the future challenges and prospects for the study of the defects of graphene and other nano-carbon materials.

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Metadata

Authors
Tian, W., Li, W., Yu, W., & Liu, X.
Venue
Micromachines
Publisher
MDPI AG
Published
2017-05-18
Volume
8
Issue
5
Pages
163
ISSN
2072-666X (ISSN-L)
OpenAlex
W2614518796
Cited by
266
References
77
FWCI
6.78
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: A Review on Lattice Defects in Graphene: Types, Generation, Effects and Regulation. https://app.cochat.ai/papers/10.3390/mi8050163
The paper
Tian, W., Li, W., Yu, W., & Liu, X. (2017). A Review on Lattice Defects in Graphene: Types, Generation, Effects and Regulation. Micromachines. https://doi.org/10.3390/mi8050163

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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.