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Cited by 488

The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.

Nucleation: theory and applications to protein solutions and colloidal suspensions

Open access
Sear, Richard P·2007·Journal of Physics: Condensed Matter

Abstract

The status of our understanding of nucleation is reviewed. Both general aspects of nucleation and those specific to protein solutions and colloidal suspensions are considered. We conclude that although we have what we believe is a quite good understanding of homogeneous nucleation in one simple system, hard-sphere colloids, in other systems there are still basic questions that are unanswered. For example, for even the most studied protein, lysozyme, there is an ongoing debate about whether the observed nucleation is homogeneous or heterogeneous. We review theoretical and simulation work on both homogeneous and heterogeneous nucleation. As heterogeneous nucleation appears to be much more common than homogeneous nucleation, and as earlier reviews have tended to focus more on homogeneous nucleation, we place particular emphasis on heterogeneous nucleation.

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Crossref · 10.1088/0953-8984/19/3/033101

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Metadata

Authors
Sear, R. P.
Venue
Journal of Physics: Condensed Matter
Published
2007-01-03
Volume
19
Issue
3
Pages
033101
ISSN
  • 0953-8984 (ISSN-L)
  • 1361-648X
OpenAlex
W2010202920
Cited by
488
References
70
FWCI
9.69
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Nucleation: theory and applications to protein solutions and colloidal suspensions. https://app.cochat.ai/papers/10.1088/0953-8984/19/3/033101
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Sear, R. P. (2007). Nucleation: theory and applications to protein solutions and colloidal suspensions. Journal of Physics: Condensed Matter. https://doi.org/10.1088/0953-8984/19/3/033101

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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.