Enhancing antioxidant properties of CeO₂ nanoparticles with Nd³⁺ doping: structural, biological, and machine learning insights

Abstract

The antioxidant capabilities of nanoparticles are contingent upon various factors, including their shape, size, and chemical composition. Herein, novel Nd-doped CeO₂ nanoparticles were synthesized and the neodymium content was varied to investigate the synergistic impact on the antioxidant properties of CeO₂ nanoparticles. Incorporating Nd³⁺ induced changes in lattice parameters and significantly altered the morphology from nanoparticles to nanorods. The biological activity of Nd-doped CeO₂ was examined against pathogenic bacterial strains, breast cancer cell lines, and antioxidant models. The antibacterial and anticancer activities of nanoparticles were not observed, which could be associated with the Ce³⁺/Ce⁴⁺ ratio. Notably, the incorporation of neodymium improved the antioxidant capacity of CeO₂. Machine learning techniques were employed to forecast the antioxidant activity to enhance understanding and predictive capabilities. Among these models, the random forest model exhibited the highest accuracy at 96.35%, establishing it as a robust computational tool for elucidating the biological behavior of Nd-doped CeO₂ nanoparticles. This study presents the first exploration of the influence of Nd³⁺ on the structural, optical, and biological attributes of CeO₂, contributing valuable insights and extending the application of machine learning in predicting the therapeutic efficacy of inorganic nanomaterials.