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Cited by 526

The DOI matches its Crossref record; 218 of the 218 references it cites were found in a registry or an index.

Chemically prepared magnetic nanoparticles

Willard, M.A., Kurihara, L.K., Carpenter, E.E., Calvin, S., Harris, V.G.·2004·International Materials Reviews

Abstract

Nanotechnology has spurred efforts to design and produce nanoscale components for incorporation into devices. Magnetic nanoparticles are an important class of functional materials, possessing unique magnetic properties due to their reduced size (below 100 nm) with potential for use in devices with reduced dimensions. Recent advances in processing by chemical synthesis and the characterisation of magnetic nanoparticles are the focus of this review. Emphasis has been placed on the various solution chemistry techniques used to synthesise particles, including: precipitation, borohydride reduction, hydrothermal, reverse micelles, polyol, sol–gel, thermolysis, photolysis, sonolysis, multisynthesis processing and electrochemical techniques. The challenges and methods for examining the structural, morphological, and magnetic properties of these materials are described.

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Crossref · 10.1179/095066004225021882

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Metadata

Authors
Willard, M. A., Kurihara, L. K., Carpenter, E. E., Calvin, S., & Harris, V. G.
Venue
International Materials Reviews
Published
2004-06
Volume
49
Issue
3-4
Pages
125-170
ISSN
  • 0950-6608 (ISSN-L)
  • 1743-2804
OpenAlex
W2952161273
Cited by
526
References
218
FWCI
12.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Chemically prepared magnetic nanoparticles. https://app.cochat.ai/papers/10.1179/095066004225021882
The paper
Willard, M. A., Kurihara, L. K., Carpenter, E. E., Calvin, S., & Harris, V. G. (2004). Chemically prepared magnetic nanoparticles. International Materials Reviews. https://doi.org/10.1179/095066004225021882

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.