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Cited by 909

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Large, Solution-Processable Graphene Quantum Dots as Light Absorbers for Photovoltaics

Yan, Xin, Cui, Xiao, Li, Binsong, Li, Liang-shi·2010·Nano Letters

Abstract

Graphenes have very attractive properties for photovoltaics. Their tunable bandgap and large optical absorptivity are desirable for efficient light harvesting. Their electronic levels and interfacing with other materials for charge transfer processes can both be tuned with well-developed carbon chemistry. Graphenes have also been shown to have very large charge mobilities, which could be useful for charge collection in solar cells. In addition, they consist of elements abundant on Earth and are environmentally friendly. However, these important properties have not been taken advantage of because graphenes that are large enough to be useful for photovoltaics have extremely poor solubility and have a strong tendency to aggregate into graphite. Here we present a novel solubilization strategy for large graphene nanostructures. It has enabled us to synthesize solution-processable, black graphene quantum dots with uniform size through solution chemistry, and we show that they can be used as sensitizers for solar cells.

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Metadata

Authors
Yan, X., Cui, X., Li, B., & Li, L.-S.
Venue
Nano Letters
Published
2010-04-08
Volume
10
Issue
5
Pages
1869-1873
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W2048385187
Cited by
909
References
4
FWCI
25.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Large, Solution-Processable Graphene Quantum Dots as Light Absorbers for Photovoltaics. https://app.cochat.ai/papers/10.1021/nl101060h
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Yan, X., Cui, X., Li, B., & Li, L.-S. (2010). Large, Solution-Processable Graphene Quantum Dots as Light Absorbers for Photovoltaics. Nano Letters. https://doi.org/10.1021/nl101060h

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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.