Thermal activation in statistical clusters of magnetic nanoparticles
Thermal activation in statistical clusters of magnetic nanoparticles
This article presents a kinetic Monte-Carlo study of thermally activated magnetisation dynamics in clusters of statistically distributed magnetic nanoparticles. The structure of clusters is assumed to be of fractal nature, consistently with recent observations of magnetic particle aggregation in cellular environments. The computed magnetisation relaxation decay and frequency-dependent hysteresis loops are seen to significantly depend on the fractal dimension of aggregates, leading to accelerated magnetisation relaxation and reduction in the size of hysteresis loops as the fractal dimension increases from one-dimensional-like to three-dimensional-like clusters. Discussed are implications for applications in nanomedicine, such as magnetic hyperthermia or magnetic particle imaging.
Hovorka, Ondrej
a12bd550-ad45-4963-aa26-dd81dd1609ee
4 January 2017
Hovorka, Ondrej
a12bd550-ad45-4963-aa26-dd81dd1609ee
Hovorka, Ondrej
(2017)
Thermal activation in statistical clusters of magnetic nanoparticles.
Journal of Physics D: Applied Physics, 50 (4), [044004].
(doi:10.1088/1361-6463/aa5066).
Abstract
This article presents a kinetic Monte-Carlo study of thermally activated magnetisation dynamics in clusters of statistically distributed magnetic nanoparticles. The structure of clusters is assumed to be of fractal nature, consistently with recent observations of magnetic particle aggregation in cellular environments. The computed magnetisation relaxation decay and frequency-dependent hysteresis loops are seen to significantly depend on the fractal dimension of aggregates, leading to accelerated magnetisation relaxation and reduction in the size of hysteresis loops as the fractal dimension increases from one-dimensional-like to three-dimensional-like clusters. Discussed are implications for applications in nanomedicine, such as magnetic hyperthermia or magnetic particle imaging.
Text
hovorka_JPD_accepted
- Accepted Manuscript
More information
Accepted/In Press date: 23 November 2016
e-pub ahead of print date: 3 January 2017
Published date: 4 January 2017
Organisations:
Computational Engineering & Design Group
Identifiers
Local EPrints ID: 407397
URI: http://eprints.soton.ac.uk/id/eprint/407397
ISSN: 0022-3727
PURE UUID: bbb8a68d-4d66-49f5-9f9b-94cd1dd7b1f2
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Date deposited: 05 Apr 2017 01:07
Last modified: 16 Mar 2024 05:12
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