Abstract
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 (CeO(2)) nanoparticles with various La concentrations (0, 1, 5, and 10 at.%). Ultrasonic-assisted synthesis enabled La incorporation into the CeO(2) 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 Ce(3+) to Ce(4+). 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 H(2)O(2) 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 CeO(2) 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.