SCHEDULED MAINTENANCE
Enhancing antioxidant properties of CeO2 nanoparticles with Nd3+ doping: structural, biological, and machine learning insights†
a
Diego E.
Navarro-López,
b
Jorge L.
Mejía-Méndez,
*c
Gildardo
Sanchez-Ante,
b
Vicente
Rodríguez-González,
d
Naveen
Tiwari
*g
and
Edgar R.
López-Mena
*b
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.
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