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Cited by 1,447

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Practical Chemical Sensors from Chemically Derived Graphene

Fowler, Jesse D., Allen, Matthew J., Tung, Vincent C., Yang, Yang, Kaner, Richard B., Weiller, Bruce H.·2009·ACS Nano

Abstract

We report the development of useful chemical sensors from chemically converted graphene dispersions using spin coating to create single-layer films on interdigitated electrode arrays. Dispersions of graphene in anhydrous hydrazine are formed from graphite oxide. Preliminary results are presented on the detection of NO(2), NH(3), and 2,4-dinitrotoluene using this simple and scalable fabrication method for practical devices. Current versus voltage curves are linear and ohmic in all cases, studied independent of metal electrode or presence of analytes. The sensor response is consistent with a charge transfer mechanism between the analyte and graphene with a limited role of the electrical contacts. A micro hot plate sensor substrate is also used to monitor the temperature dependence of the response to nitrogen dioxide. The results are discussed in light of recent literature on carbon nanotube and graphene sensors.

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Metadata

Authors
Fowler, J. D., Allen, M. J., Tung, V. C., Yang, Y., Kaner, R. B., & Weiller, B. H.
Venue
ACS Nano
Published
2009-01-27
Volume
3
Issue
2
Pages
301-306
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2112835738
Cited by
1,447
References
2
FWCI
79.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Practical Chemical Sensors from Chemically Derived Graphene. https://app.cochat.ai/papers/10.1021/nn800593m
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Fowler, J. D., Allen, M. J., Tung, V. C., Yang, Y., Kaner, R. B., & Weiller, B. H. (2009). Practical Chemical Sensors from Chemically Derived Graphene. ACS Nano. https://doi.org/10.1021/nn800593m

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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.