Experimental investigation and machine learning modeling of the effects of hybridization mixing ratio, nanoparticle type, and temperature on the thermophysical properties of Fe3O4/TiO2, Fe3O4/MgO, and Fe3O4/ZnO-DI water hybrid ferrofluids

Adogbeji, Victor OmoefeAtofarati, Emmanuel O.Sharifpur, MohsenMeyer, Joshua P.2025-08-072025-08-072025-07Adogbeji, V.O., Atofarati, E.O., Sharifpur, M. et al. Experimental investigation and machine learning modeling of the effects of hybridization mixing ratio, nanoparticle type, and temperature on the thermophysical properties of Fe3O4/TiO2, Fe3O4/MgO, and Fe3O4/ZnO-DI water hybrid ferrofluids. Journal of Thermal Analysis and Calorimetry 150, 10549–10573 (2025). https://doi.org/10.1007/s10973-025-14399-y.1388-6150 (print)1588-2926 (online)10.1007/s10973-025-14399-yhttp://hdl.handle.net/2263/103814Please read abstract in the article.en© The Author(s) 2025. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.Magnetic hybrid ferrofluids (MHFs)Thermoelectric conductivity (TEC)StabilityHybridization mixing ratiosViscosityHeat transfer efficiencyExperimental investigation and machine learning modeling of the effects of hybridization mixing ratio, nanoparticle type, and temperature on the thermophysical properties of Fe3O4/TiO2, Fe3O4/MgO, and Fe3O4/ZnO-DI water hybrid ferrofluidsArticle