Simulation of thermally fluctuating magnetic nanoparticles for hyperthermia

Abstract

This thesis details the design, implementation, and application of numerical methods for simulating the stochastic dynamics of magnetic nanoparticles. In particular, the experiments in this thesis simulate the heat dissipated by magnetic nanoparticles when subjected to alternating magnetic fields. The nonlinear dynamics of these systems are elevant to hyperthermia, a clinical technique that uses localised particles to increase the temperature of tumour tissue and trigger cell death.
The simulations are implemented in an open-source software package, which focuses on reproducibility of results and rigorous testing of the underlying algorithms. In particular, the numerical errors of popular numerical schemes for simulating the stochastic Landau-Lifshitz-Gilbert equation are evaluated. These results suggest a trade-off between accuracy and computational complexity in systems with strong thermal fluctuations. The imulations are used to investigate the effects of novel applied waveform shapes and arbitrary shaped clusters of interacting particles.
The simulation results show that careful consideration of the applied field characteristics and clustering of particles can both enhance and diminish heat dissipation in magnetic nanoparticles. Large chains of particles aligned with the applied field are capable of very efficient heat dissipation but their strong dependence on the orientation of the applied field could lead to spatial variations in heating. Small dense clusters are shown to consistently perform worse than a single, isolated nanoparticle. Additionally, the use of
square-wave applied magnetic fields are shown to improve the robustness of hyperthermia procedures and potentially increase heat dissipation when compared with traditional sinusoidal fields.

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Identifiers

ORCID for Ondrej Hovorka: orcid.org/0000-0002-6707-4325

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