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Cited by 175

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Silver Nanoparticle Formation: Predictions and Verification of the Aggregative Growth Model

Van Hyning, Dirk L., Klemperer, Walter G., Zukoski, Charles F.·2001·Langmuir

Abstract

The mechanisms of growth of 5−30 nm silver particles produced from the reduction of silver perchlorate by sodium borohydride are explored. Evidence is provided that, within 5 s of mixing of silver perchlorate with borohydride, the concentration of ionic silver has dropped 2 orders of magnitude, initially resulting in the formation of ∼2.5 nm silver particles. We hypothesize that growth takes place over the next 20−50 min by an aggregative growth mechanism. This hypothesis is tested by comparing model predictions with experimental results of particle growth rates over a range of ionic strengths and reaction temperatures. The model assumes particles interact via electrostatic and van der Waals forces, and comparison of numerical solutions to population balance models for aggregation and coalescence demonstrates that particle growth rates and final sizes can be predicted with surface potentials within 20% of those previously reported under reaction conditions.

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Metadata

Authors
Van Hyning, D. L., Klemperer, W. G., & Zukoski, C. F.
Venue
Langmuir
Published
2001-05-01
Volume
17
Issue
11
Pages
3128-3135
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
OpenAlex
W2086530777
Cited by
175
References
1
FWCI
2.93
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Silver Nanoparticle Formation: Predictions and Verification of the Aggregative Growth Model. https://app.cochat.ai/papers/10.1021/la000856h
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Van Hyning, D. L., Klemperer, W. G., & Zukoski, C. F. (2001). Silver Nanoparticle Formation: Predictions and Verification of the Aggregative Growth Model. Langmuir. https://doi.org/10.1021/la000856h

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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.