Estimating the heating of complex nanoparticle aggregates for magnetic hyperthermia

TL;DR

This study uses in silico modeling to analyze how nanoparticle aggregate size and fractal geometry influence heat release in magnetic hyperthermia, aiding better treatment planning.

Contribution

It introduces a digital analysis of nanoparticle aggregates' heating behavior, accounting for size and fractal geometry, improving prediction accuracy for hyperthermia treatments.

Findings

Average heat per particle stabilizes in small aggregates

Fractal parameters significantly affect heating performance

Aggregates reduce heating power compared to non-interacting nanoparticles

Abstract

Understanding and predicting the heat released by magnetic nanoparticles is central to magnetic hyperthermia treatment planning. These nanoparticles tend to form aggregates when injected in living tissues, which alters their response to the applied alternating magnetic field and prevents predicting the released heat accurately. We performed an in silico analysis to investigate the heat released by nanoparticle aggregates featuring different size and fractal geometry factors. By digitally mirroring aggregates seen in biological tissues, we found that the average heat released per particle stabilizes starting from moderately small aggregates, facilitating the estimates for their larger counterparts. Additionally, we studied the heating performance of particle aggregates over a wide range of fractal parameters. We compared this result with the heat released by non-interacting nanoparticles to quantify the reduction of heating power after being instilled into tissues. This set of results can be used to estimate the expected heating in vivo based on the experimentally determined nanoparticle properties.