# Giant-spin nonlinear response theory of magnetic nanoparticle   hyperthermia: a field dependence study

**Authors:** Marcus S. Carri\~ao, Victor R. R. Aquino, Gabriel T. Landi, Ediron L., Verde, Marcelo H. Sousa, Andris F. Bakuzis

arXiv: 1702.02022 · 2017-05-24

## TL;DR

This paper introduces a nonlinear response model for magnetic nanoparticles that accurately predicts heat loss at high field amplitudes, improving hyperthermia treatment planning and related biomedical applications.

## Contribution

The authors develop a nonlinear magnetization and heat loss model for larger single-domain nanoparticles, extending beyond linear response theory and validated with experimental data.

## Key findings

- Model predicts shift in optimal particle size for heat loss.
- Heat loss field amplitude exponents are distinct at high fields.
- Experimental data supports the nonlinear response model.

## Abstract

Understanding high-field amplitude electromagnetic heat loss phenomena is of great importance, in particular in the biomedical field, since the heat-delivery treatment plans might rely on analytical models that are only valid at low field amplitudes. Here, we develop a nonlinear response model valid for single- domain nanoparticles of larger particle sizes and higher field amplitudes in comparison to linear response theory. A nonlinear magnetization expression and a generalized heat loss power equation are obtained and compared with the exact solution of the stochastic Landau-Lifshitz-Gilbert equation assuming the giant-spin hypothesis. The model is valid within the hyperthermia therapeutic window and predicts a shift of optimum particle size and distinct heat loss field amplitude exponents. Experimental hyperthermia data with distinct ferrite-based nanoparticles, as well as third harmonic magnetization data supports the nonlinear model, which also has implications for magnetic particle imaging and magnetic thermometry.

## Full text

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## Figures

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## References

52 references — full list in the complete paper: https://tomesphere.com/paper/1702.02022/full.md

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Source: https://tomesphere.com/paper/1702.02022