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Cited by 304

The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.

Tailoring Electronic Properties of Graphene by π–π Stacking with Aromatic Molecules

Zhang, Zengxing, Huang, Helin, Yang, Xiaomei, Zang, Ling·2011·The Journal of Physical Chemistry Letters

Abstract

Intrinsic graphene is a semimetal or zero bandgap semiconductor, which hinders its applications for nanoelectronics. To develop high-performance nanodevices with graphene, it is necessary to open the bandgap and precisely control the charge carrier type and density. In this perspective, we focus on tailoring the electronic properties of graphene by noncovalent stacking with aromatic molecules through π–π interaction. Different types of molecules (functioning as either an electron donor or acceptor when stacked with graphene) as reported in recent literature are presented regarding surface patterning, bandgap engineering, surface doping, as well as applications in nanodevices, particularly the field-effect transistors (FETs). On the basis of the current progress along this research line, future issues and challenges are also briefly discussed.

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Metadata

Authors
Zhang, Z., Huang, H., Yang, X., & Zang, L.
Venue
The Journal of Physical Chemistry Letters
Published
2011-11-03
Volume
2
Issue
22
Pages
2897-2905
ISSN
1948-7185 (ISSN-L)
OpenAlex
W2330700472
Cited by
304
References
41
FWCI
7.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Tailoring Electronic Properties of Graphene by π–π Stacking with Aromatic Molecules. https://app.cochat.ai/papers/10.1021/jz201273r
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Zhang, Z., Huang, H., Yang, X., & Zang, L. (2011). Tailoring Electronic Properties of Graphene by π–π Stacking with Aromatic Molecules. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/jz201273r

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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.