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Cited by 2,017

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Highly conducting graphene sheets and Langmuir–Blodgett films

Open access
Li, Xiaolin, Zhang, Guangyu, Bai, Xuedong, Sun, Xiaoming, Wang, Xinran, Wang, Enge, Dai, Hongjie·2008·Nature Nanotechnology

Abstract

Graphene is an intriguing material with properties that are distinct from those of other graphitic systems. The first samples of pristine graphene were obtained by 'peeling off' and epitaxial growth. Recently, the chemical reduction of graphite oxide was used to produce covalently functionalized single-layer graphene oxide. However, chemical approaches for the large-scale production of highly conducting graphene sheets remain elusive. Here, we report that the exfoliation-reintercalation-expansion of graphite can produce high-quality single-layer graphene sheets stably suspended in organic solvents. The graphene sheets exhibit high electrical conductance at room and cryogenic temperatures. Large amounts of graphene sheets in organic solvents are made into large transparent conducting films by Langmuir-Blodgett assembly in a layer-by-layer manner. The chemically derived, high-quality graphene sheets could lead to future scalable graphene devices.

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Crossref · 10.1038/nnano.2008.210

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Metadata

Authors
Li, X., Zhang, G., Bai, X., Sun, X., Wang, X., Wang, E., & Dai, H.
Venue
Nature Nanotechnology
Published
2008-08-01
Volume
3
Issue
9
Pages
538-542
ISSN
  • 1748-3387 (ISSN-L)
  • 1748-3395
OpenAlex
W2147789376
Cited by
2,017
References
7
FWCI
74.61
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Highly conducting graphene sheets and Langmuir–Blodgett films. https://app.cochat.ai/papers/10.1038/nnano.2008.210
The paper
Li, X., Zhang, G., Bai, X., Sun, X., Wang, X., Wang, E., & Dai, H. (2008). Highly conducting graphene sheets and Langmuir–Blodgett films. Nature Nanotechnology. https://doi.org/10.1038/nnano.2008.210

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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.