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Modelling of Dynamic Behaviour in Magnetic Nanoparticles

The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate descri...

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Autores principales: Rietberg, Max Tigo, Waanders, Sebastiaan, Horstman-van de Loosdrecht, Melissa Mathilde, Wildeboer, Rogier R., ten Haken, Bennie, Alic, Lejla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708731/
https://www.ncbi.nlm.nih.gov/pubmed/34947745
http://dx.doi.org/10.3390/nano11123396
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author Rietberg, Max Tigo
Waanders, Sebastiaan
Horstman-van de Loosdrecht, Melissa Mathilde
Wildeboer, Rogier R.
ten Haken, Bennie
Alic, Lejla
author_facet Rietberg, Max Tigo
Waanders, Sebastiaan
Horstman-van de Loosdrecht, Melissa Mathilde
Wildeboer, Rogier R.
ten Haken, Bennie
Alic, Lejla
author_sort Rietberg, Max Tigo
collection PubMed
description The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker–Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag(®)-D70, Synomag(®)-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation.
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spelling pubmed-87087312021-12-25 Modelling of Dynamic Behaviour in Magnetic Nanoparticles Rietberg, Max Tigo Waanders, Sebastiaan Horstman-van de Loosdrecht, Melissa Mathilde Wildeboer, Rogier R. ten Haken, Bennie Alic, Lejla Nanomaterials (Basel) Article The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker–Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag(®)-D70, Synomag(®)-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation. MDPI 2021-12-15 /pmc/articles/PMC8708731/ /pubmed/34947745 http://dx.doi.org/10.3390/nano11123396 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rietberg, Max Tigo
Waanders, Sebastiaan
Horstman-van de Loosdrecht, Melissa Mathilde
Wildeboer, Rogier R.
ten Haken, Bennie
Alic, Lejla
Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title_full Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title_fullStr Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title_full_unstemmed Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title_short Modelling of Dynamic Behaviour in Magnetic Nanoparticles
title_sort modelling of dynamic behaviour in magnetic nanoparticles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708731/
https://www.ncbi.nlm.nih.gov/pubmed/34947745
http://dx.doi.org/10.3390/nano11123396
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