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Cited by 342

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

Chemical Doping of Large-Area Stacked Graphene Films for Use as Transparent, Conducting Electrodes

Kasry, Amal, Kuroda, Marcelo A., Martyna, Glenn J., Tulevski, George S., Bol, Ageeth A.·2010·ACS Nano

Abstract

Graphene is considered a leading candidate to replace conventional transparent conducting electrodes because of its high transparency and exceptional transport properties. The effect of chemical p-type doping on graphene stacks was studied in order to reduce the sheet resistance of graphene films to values approaching those of conventional transparent conducting oxides. In this report, we show that large-area, stacked graphene films are effectively p-doped with nitric acid. The doping decreases the sheet resistance by a factor of 3, yielding films comprising eight stacked layers with a sheet resistance of 90 Omega/(square) at a transmittance of 80%. The films were doped either after all of the layers were stacked (last-layer-doped) or after each layer was added (interlayer-doped). A theoretical model that accurately describes the stacked graphene film system as a resistor network was developed. The model defines a characteristic transfer length where all the channels in the graphene films actively contribute to electrical transport. The experimental data shows a linear increase in conductivity with the number of graphene layers, indicating that each layer provides an additional transport channel, in good agreement with the theoretical model.

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Metadata

Authors
Kasry, A., Kuroda, M. A., Martyna, G. J., Tulevski, G. S., & Bol, A. A.
Venue
ACS Nano
Published
2010-07-12
Volume
4
Issue
7
Pages
3839-3844
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2085462202
Cited by
342
References
27
FWCI
13.28
Type
journal article
Language
en
Open access
closed

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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.