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

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High Resolution Image Download MS PowerPoint Slide This study presents a comprehensive analysis of the photothermal therapy (PTT) performances of single gold (Au), single magnetite (Fe 3 O 4 ), and Au@Fe 3 O 4 and Fe 3 O 4 @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 Fe 3 O 4 nanoparticles exhibit a higher intrinsic photothermal efficiency than Au; however, integrating both materials in core–shell structures markedly enhances thermal responses, with Fe 3 O 4 @Au consistently outperforming Au@Fe 3 O 4 . 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 J o values were observed at a ratio of 5:1. Among the optical parameters, the absorption cross-section ( C abs ) had the strongest influence on the photothermal performance, while J 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.