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Cited by 1,645

The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

Tailoring Surface Plasmons through the Morphology and Assembly of Metal Nanoparticles

Liz-Marzán, Luis M.·2006·Langmuir
DOI 10.1021/la0513353
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Abstract

Metal nanoparticles can be used as building blocks for the formation of nanostructured materials. For the design of materials with specific (optical) properties, several approaches can be followed, even when starting from the very same basic units. In this article, a survey is provided of the optical properties of noble metal nanoparticles, specifically gold, silver, and their combinations, prepared in solution through colloid chemical methods. The optical properties are shown to be mainly influenced by the surface plasmon resonance of conduction electrons, the frequency of which is not only determined by the nature of the metal but also by a number of other parameters, such as particle size and shape, the presence of a capping shell on the particle surface, or the dielectric properties of the surrounding medium. Recent results showing how these various parameters affect the optical properties are reviewed. The results highlight the high degree of control that can now be achieved through colloid chemical synthesis.

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Metadata

Authors
Liz-Marzán, L. M.
Venue
Langmuir
Publisher
American Chemical Society (ACS)
Published
2005-08-27
Volume
22
Issue
1
Pages
32-41
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
DOI
10.1021/la0513353
OpenAlex
W2076849967
Cited by
1,645
References
83
FWCI
31.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Tailoring Surface Plasmons through the Morphology and Assembly of Metal Nanoparticles. https://app.cochat.ai/papers/10.1021/la0513353
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Liz-Marzán, L. M. (2006). Tailoring Surface Plasmons through the Morphology and Assembly of Metal Nanoparticles. Langmuir. https://doi.org/10.1021/la0513353

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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.