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Cited by 302

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Chemical Preparation of Graphene‐Based Nanomaterials and Their Applications in Chemical and Biological Sensors

Jiang, Hongji·2011·Small

Abstract

Graphene is a flat monolayer of carbon atoms packed tightly into a 2D honeycomb lattice that shows many intriguing properties meeting the key requirements for the implementation of highly excellent sensors, and all kinds of proof‐of‐concept sensors have been devised. To realize the potential sensor applications, the key is to synthesize graphene in a controlled way to achieve enhanced solution‐processing capabilities, and at the same time to maintain or even improve the intrinsic properties of graphene. Several production techniques for graphene‐based nanomaterials have been developed, ranging from the mechanical cleavage and chemical exfoliation of high‐quality graphene to direct growth onto different substrates and the chemical routes using graphite oxide as a precusor to the newly developed bottom‐up approach at the molecular level. The current review critically explores the recent progress on the chemical preparation of graphene‐based nanomaterials and their applications in sensors.

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Metadata

Authors
Jiang, H.
Venue
Small
Publisher
Wiley
Published
2011-06-03
Volume
7
Issue
17
Pages
2413-2427
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2127658056
Cited by
302
References
6
FWCI
14.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Chemical Preparation of Graphene‐Based Nanomaterials and Their Applications in Chemical and Biological Sensors. https://app.cochat.ai/papers/10.1002/smll.201002352
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Jiang, H. (2011). Chemical Preparation of Graphene‐Based Nanomaterials and Their Applications in Chemical and Biological Sensors. Small. https://doi.org/10.1002/smll.201002352

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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.