Datos de investigación

Data for "Theoretical Investigation of Nanoparticle Relaxation for Magnetic Hyperthermia Applications: Effects of Size, Anisotropy, and Interparticle Distance Distributions"

Valdés, Daniela Paola Icon; Lima, Enio Junior Icon; Zysler, Roberto Daniel Icon; de Biasi, Emilio Icon

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Fecha

Publicador:

Consejo Nacional de Investigaciones Científicas y Técnicas

Fecha de depósito:

12/03/2025

Fecha de creación:

1/1/2022-31/12/2023

Resumen

Theoretical models on the relaxation of magnetic nanoparticles (MNPs) are essential for designing optimized systems for biomedical applications like Magnetic Hyperthermia (MH). However, existing models often assume uniform, ideal MNPs in non-interacting systems or simple, equidistant MNP assemblies, which do not accurately reflect experimental complexities. Parameter distributions are expected to modify the AC hysteresis loop of the system and, consequently, the power absorption from the magnetic field, as it is proportional to its enclosed area. However, studies on this impact remain limited. In this article, we investigate the magnetic relaxation in MH conditions of non-interacting MNP systems with typical experimental size and anisotropy-constant distributions for two easy-axis configurations: oriented parallel to the applied field and randomly. Additionally, we analyze MNPs arranged into chains with a dispersion in interparticle distances. Our findings reveal that the irreversibility of hysteresis loops increases with the standard deviation of all distributions considered when easy-axes are aligned with the external magnetic field, but this effect is diminished by random orientation. Furthermore, we correlate internal structures formed by the dispersion of interparticle distances in chains with said increase of irreversibility. Coercivity is modified by size distributions, while susceptibility by anisotropy constant distributions. As irreversibility, coercivity and susceptibility impact on the hysteresis loop area, studying their change with realistic parameter distributions is critical to improve and understand MH performance, filling the gap between theory and experiment.

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