From sonochemical synthesis to predictive modeling: Unraveling the antioxidant properties of La-doped CeO2 nanoparticles

This study introduces a novel combination of sonochemical synthesis and machine learning (ML) modeling to analyze the effect of Lanthanum (La) doping on the biological properties of cerium oxide (CeO₂) nanoparticles with various La concentrations (0, 1, 5, and 10 at.%). Ultrasonic-assisted synthesis enabled La incorporation into the CeO₂ lattice, resulting changes in crystallinity, lattice parameters, and surface features. Detailed characterization confirmed successful doping and indicated stable nanoparticle suspensions with controlled size ranges. Sonochemical synthesis promoted the oxidation of Ce³⁺ to Ce⁴⁺. Biological testing revealed low cytotoxicity across various cell lines (HepG-2, 3 T3-L1, Caco-2, and U87) and increased antioxidant activity, especially in samples with 5 and 10 at.% La, which demonstrated improved free radical scavenging of DPPH and H₂O₂ radicals. Notably, advanced ML models-including Extremely Randomized Trees, random forest, Gradient Boosting, and LightGBM-enabled the prediction of antioxidant activity based on nanoparticle features, identifying antioxidant method, concentration, and chemical composition as key factors influencing biological effects. This combined experimental and data-driven approach not only clarifies the structure-activity relationships of La-doped CeO₂ nanoparticles but also emphasizes the significant potential of ML in designing and optimizing nanomaterials for biomedical applications. The combination of sonochemical synthesis and ML modeling provides a robust framework for accelerating nanomaterial development by minimizing trial-and-error experiments and providing mechanistic insights into their biological functions.