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Cited by 432

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Colloidal nanoparticle clusters: functional materials by design

Lu, Zhenda, Yin, Yadong·2012·Chemical Society Reviews

Abstract

Significant advances in colloidal synthesis made in the past two decades have enabled the preparation of high quality nanoparticles with well-controlled sizes, shapes, and compositions. It has recently been realized that such nanoparticles can be utilized as 'artificial atoms' for building new materials which not only combine the size- and shape-dependent properties of individual nanoparticles but also create new collective properties by taking advantage of their electromagnetic interactions. The controlled clustering of nanoparticle building blocks into defined geometric arrangements opens a new research area in materials science and as a result much interest has been paid to the creation of secondary structures of nanoparticles, either by direct solution growth or self-assembly methods. In this tutorial review, we introduce recently developed strategies for the creation and surface modification of colloidal nanoparticle clusters, demonstrate the new collective properties resulting from their secondary structures, and highlight several of their many important technological applications ranging from photonics, separation, and detection, to multimodal imaging, energy storage and transformation, and catalysis.

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Metadata

Authors
Lu, Z., & Yin, Y.
Venue
Chemical Society Reviews
Published
2012
Volume
41
Issue
21
Pages
6874
ISSN
  • 0306-0012 (ISSN-L)
  • 1460-4744
OpenAlex
W2016414716
Cited by
432
References
3
FWCI
1.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Colloidal nanoparticle clusters: functional materials by design. https://app.cochat.ai/papers/10.1039/c2cs35197h
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Lu, Z., & Yin, Y. (2012). Colloidal nanoparticle clusters: functional materials by design. Chemical Society Reviews. https://doi.org/10.1039/c2cs35197h

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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.