Predictive Modeling of Core–Shell Magnetoplasmonic Nanoparticles: GPR-Based Optimization for Enhanced Photothermal Heating

This study presents a comprehensive analysis of the photothermal therapy (PTT) performances of single gold (Au), single magnetite (Fe₃O₄), and Au@Fe₃O₄ and Fe₃O₄@Au core-shell nanoparticles using PyMieLab and COMSOL Multiphysics simulations. Through a detailed parametric study, we elucidate how core-shell configurations influence photothermal conversion efficiency, heat generation, and spatial temperature distribution. Our results reveal that Fe₃O₄ nanoparticles exhibit a higher intrinsic photothermal efficiency than Au; however, integrating both materials in core-shell structures markedly enhances thermal responses, with Fe₃O₄@Au consistently outperforming Au@Fe₃O₄. This enhancement arises from synergistic core-shell interactions that optimize light absorption and thermal conversion. A Gaussian process regression (GPR)-based efficiency parameter was introduced to predict optimal geometric combinations and temperature profiles. The analysis revealed clear design rules; the effective shell thicknesses ranged from 10 to 30 nm, while the most efficient core radius was around 80 nm. The highest predicted temperature elevations occurred at a 4:1 core-to-shell ratio, although the largest <i>J</i> _o values were observed at a ratio of 5:1. Among the optical parameters, the absorption cross-section (<i>C</i> _abs) had the strongest influence on the photothermal performance, while <i>J</i> _o, despite its limited predictive reliability in the current data set, remains a promising descriptor for future studies with broader material combinations. These findings underscore the pivotal role of nanoparticle design in PTT optimization.