Predicting size-dependent heating efficiency of magnetic nanoparticles from experiment and stochastic Néel-Brown Langevin simulation

Abstract Magnetic nanoparticles (MNP) have been investigated for generating therapeutic heat when subjected to an alternating magnetic field (AMF) and applied for tumor-confined cancer therapy, so-called magnetic fluid hyperthermia (MFH). For application of MFH, a key requirement is the reduction of MNP dosing by maximizing the heat generation within medically safe limits of the applied AMF. Therefore, reliable and accurate predictions of particle heating are required for the advancement of therapy planning. In this study, we compare size-dependent particle heating data from calorimetric measurements to stochastic Neel-Brown Langevin equation Monte Carlo simulations, finding good agreement between them for various AMF amplitudes and frequencies. Within medical safety constraints of the AMF, our simulations predict maximum particle heating for magnetite particle core sizes above 25  nm with effective anisotropy constants K = 4000  J/m 3 at frequencies of ∼ 100  kHz and field amplitudes ∼ 10  mT/μ 0 . These simulations could help to predict the optimal combination of medically safe AMF parameters and MNP intrinsic properties, such as core size and effective anisotropy, to maximize heat generation and reduce MNP dosing in the application of MFH.