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Issue 10, 2017
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Anticipating hyperthermic efficiency of magnetic colloids using a semi-empirical model: a tool to help medical decisions

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Abstract

Magnetic hyperthermia, a modality that uses radio frequency heating assisted with single-domain magnetic nanoparticles, is becoming established as a powerful oncological therapy. Much improvement in nanomaterials development, to enhance their heating efficiency by tuning the magnetic colloidal properties, has been achieved. However, methodological standardization to accurately and univocally determine the colloidal properties required to numerically reproduce a specific heating efficiency using analytical expressions still holds. Thus, anticipating the hyperthermic performances of magnetic colloids entails high complexity due to polydispersity, aggregation and dipolar interactions always present in real materials to a greater or lesser degree. Here, by numerically simulating the experimental results and using real biomedical aqueous colloids, we analyse and compare several approaches to reproduce experimental specific absorption rate values. Then, we show that the relaxation time, determined using a representative mean activation energy consistently derived from four independent experiments accurately reproduces experimental heating efficiencies. Moreover, the so-derived relaxation time can be used to extrapolate the heating performance of the magnetic nanoparticles to the other field conditions within the framework of the linear response theory. We thus present a practical tool that may truly aid the design of medical decisions.

Graphical abstract: Anticipating hyperthermic efficiency of magnetic colloids using a semi-empirical model: a tool to help medical decisions

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Publication details

The article was received on 24 Nov 2016, accepted on 07 Feb 2017 and first published on 07 Feb 2017


Article type: Paper
DOI: 10.1039/C6CP08059F
Citation: Phys. Chem. Chem. Phys., 2017,19, 7176-7187
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    Anticipating hyperthermic efficiency of magnetic colloids using a semi-empirical model: a tool to help medical decisions

    M. B. Fernández van Raap, D. F. Coral, S. Yu, G. A. Muñoz, F. H. Sánchez and A. Roig, Phys. Chem. Chem. Phys., 2017, 19, 7176
    DOI: 10.1039/C6CP08059F

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