Kinetic Monte Carlo Approach as a Tool for the Study of Magnetic Hyperthermia Efficiency

This study focuses on the development and implementation of a computational model based on a two-level system framework, employing a kinetic Monte Carlo approach, to simulate the behavior of magnetic nanoparticles in fluid environments under alternating magnetic field excitation. The primary goal is to develop a tool to be used in the investigation of the impact of different nanoparticle properties on the efficiency of magnetic hyperthermia. A crucial aspect of this investigation is the rigorous validation of the computational model through comprehensive comparisons with experimental observations varying particle size, concentration, and magnetic field amplitude and frequency, ensuring the reliability and accuracy of the model in capturing the complex dynamics of nanoparticles in suspension and its influence on hyperthermia efficiency. Our findings underscore the potential of this computational model as a predictive tool for estimating Specific Loss Power (SLP) metrics across various systems subjected to different applied magnetic fields. By delineating the relationship between the nanoparticle’s characteristics and hyperthermia efficiency, this study contributes to advancing the fundamental understanding and predictive capabilities of magnetic nanoparticle-based hyperthermia therapies.