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Cited by 548

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Shape and Aggregation Control of Nanoparticles: Not Shaken, Not Stirred

Li, Dan, Kaner, Richard B.·2006·Journal of the American Chemical Society

Abstract

The aggregation of nanoparticles during synthesis, particularly the effect of mechanical agitation, is investigated from a viewpoint of nucleation using a conjugated polymer, polyaniline, as an example. Homogeneous nucleation of polyaniline results in nanofibers, while heterogeneous nucleation leads to granular particulates. Mechanical agitation, which is a common method for disrupting aggregates, instead dramatically triggers aggregation during the synthetic process and favors the formation of granular particulates. Correlating the shape and aggregation of polyaniline nanoparticles with the mode of nucleation, a new aggregation mechanism is proposed in which aggregation is triggered by heterogeneous nucleation. The mechanism may be quite general as indicated by experiments with other materials such as silica nanoparticles. Highly dispersible polyaniline nanofibers can now be reproducibly prepared from a conventional reaction simply by not mechanically agitating the reaction and carrying it out at an elevated temperature. This work may prove to be of great value in reproducibly synthesizing nanoparticles with well-controlled sizes and shapes and in effectively preventing aggregation in chemical, pharmaceutical, and materials production processes.

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Metadata

Authors
Li, D., & Kaner, R. B.
Venue
Journal of the American Chemical Society
Published
2005-12-31
Volume
128
Issue
3
Pages
968-975
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2026448047
Cited by
548
References
21
FWCI
17.97
Type
journal article
Language
en
Open access
closed

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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.