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Cited by 362

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

A bottom-up approach from molecular nanographenes to unconventional carbon materials

Zhi, Linjie, Müllen, Klaus·2008·Journal of Materials Chemistry

Abstract

Strategies used to grow large graphenes and graphene-based materials through chemical approaches are discussed. Molecular graphenes are believed to be suitable precursors for growing large graphenes as well as fabricating graphene-based materials. The structure of the precursor and the thermolysis procedure determine the structure of the carbonaceous product; and a chemically driven method for the growth and integration of graphenes is thus developed. This paper gives a brief overview of the recent research works concerning chemical and thermal approaches toward large graphenes and graphene-based carbonaceous materials, including the selection of structurally-defined precursors, the template-assisted integration of molecular graphenes into unconventional carbon nanoobjects, the importance of the thermolysis procedures, and the function and application of the obtained materials.

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Metadata

Authors
Zhi, L., & Müllen, K.
Venue
Journal of Materials Chemistry
Published
2008
Volume
18
Issue
13
Pages
1472
ISSN
  • 0959-9428 (ISSN-L)
  • 1364-5501
OpenAlex
W2085308487
Cited by
362
References
5
FWCI
12.46
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A bottom-up approach from molecular nanographenes to unconventional carbon materials. https://app.cochat.ai/papers/10.1039/b717585j
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Zhi, L., & Müllen, K. (2008). A bottom-up approach from molecular nanographenes to unconventional carbon materials. Journal of Materials Chemistry. https://doi.org/10.1039/b717585j

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.