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Cited by 545

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Preparation of monodispersed metal particles

Goia, Dan V., Matijević, Egon·1998·New Journal of Chemistry

Abstract

This review article deals with the preparation, characterization and mechanisms of formation of uniform simple and composite metal particles of different modal diameters and shapes. In principle, such dispersions can be obtained by reduction of metal ions, in uncomplexed or complexed state, with suitable reducing agents and appropriate additives. The effect of the redox potential in a given oxidation/reduction system on the nature of the final particles is discussed in some detail. In another approach monodispersed particles of metal compounds are prepared first and then reduced to pure metals either in a liquid or a gaseous medium. In doing so, one can produce powders of a given morphology by selecting the precursor particles of the desired shape. A special case is represented by coated particles, consisting of cores and shells of different chemical composition. Depending on the materials, it is possible to reduce either one or both components by appropriate chemical reactions.

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Metadata

Authors
Goia, D. V., & Matijević, E.
Venue
New Journal of Chemistry
Published
1998
Volume
22
Issue
11
Pages
1203-1215
ISSN
  • 0398-9836 (ISSN-L)
  • 1144-0546
  • 1369-9261
OpenAlex
W2035519512
Cited by
545
References
2
FWCI
2.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Preparation of monodispersed metal particles. https://app.cochat.ai/papers/10.1039/a709236i
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Goia, D. V., & Matijević, E. (1998). Preparation of monodispersed metal particles. New Journal of Chemistry. https://doi.org/10.1039/a709236i

Data sources

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.