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High‐gradient magnetic separation of coated magnetic nanoparticles

Moeser, Geoffrey D., Roach, Kaitlin A., Green, William H., Alan Hatton, T., Laibinis, Paul E.·2004·AIChE Journal

Abstract

The feasibility is examined of using high‐gradient magnetic separation (HGMS) to recover about 8‐nm magnetite nanoparticles that are tailored specifically to extract target solutes (polymer‐coated nanoparticles for the extraction of soluble organic contaminants from water and phospholipid‐coated particles for the selective extraction of proteins). A general model for nanoparticle capture based on calculating the limit of static nanoparticle buildup around the wires in an HGMS column is presented. Model predictions are compared successfully with experimental results from a bench‐scale HGMS column for both the polymer‐ and phospholipid‐coated particles. A minimum diameter for successful particle capture is derived for both individually dispersed nanoparticles and aggregates of nanoparticles. The individually dispersed polymer‐coated nanoparticles are less easily captured in the HGMS column than are the phospholipid‐coated particles, which exist as submicron aggregates. © 2004 American Institute of Chemical Engineers AIChE J, 50: 2835–2848, 2004

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Metadata

Authors
Moeser, G. D., Roach, K. A., Green, W. H., Alan Hatton, T., & Laibinis, P. E.
Venue
AIChE Journal
Publisher
Wiley
Published
2004-10-08
Volume
50
Issue
11
Pages
2835-2848
ISSN
  • 0001-1541 (ISSN-L)
  • 1547-5905
OpenAlex
W2118603854
Cited by
262
References
0
FWCI
8.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: High‐gradient magnetic separation of coated magnetic nanoparticles. https://app.cochat.ai/papers/10.1002/aic.10270
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Moeser, G. D., Roach, K. A., Green, W. H., Alan Hatton, T., & Laibinis, P. E. (2004). High‐gradient magnetic separation of coated magnetic nanoparticles. AIChE Journal. https://doi.org/10.1002/aic.10270

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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.