This study measures the effective thermal conductivity of nanoparticle-fluid mixtures using a steady-state parallel-plate method. The fluids tested include water, ethylene glycol, engine oil, and vacuum pump fluid, with nanoparticles of Al₂O₃ and CuO dispersed in each. Experimental results show that the thermal conductivity of the mixtures is higher than that of the base fluids. However, theoretical models for predicting effective thermal conductivity of mixtures significantly underestimate the measured values, indicating their inadequacy for nanoparticle-fluid mixtures. The study discusses possible mechanisms for the enhanced thermal conductivity, including microscopic particle motion, particle structures, and surface properties. It also examines the effects of different dispersion techniques on the thermal conductivity of the mixtures. The results show that the thermal conductivity of the mixtures increases with decreasing particle size and varies depending on the dispersion method. Theoretical models for calculating effective thermal conductivity of mixtures are compared with experimental data. The models fail to accurately predict the measured thermal conductivity of the nanoparticle-fluid mixtures, suggesting that current models do not account for the complex energy transfer processes in such systems. The study highlights the need for a more comprehensive theory to explain the behavior of nanoparticle-fluid mixtures. The thermal conductivity of the mixtures is also found to be influenced by the viscosity of the fluid. The viscosity of the mixtures increases with the volume fraction of nanoparticles, which can affect the performance of the fluid in heat transfer applications. The study concludes that nanoparticle-fluid mixtures have potential for use in heat transfer applications, but further research is needed to fully understand their behavior and optimize their performance.This study measures the effective thermal conductivity of nanoparticle-fluid mixtures using a steady-state parallel-plate method. The fluids tested include water, ethylene glycol, engine oil, and vacuum pump fluid, with nanoparticles of Al₂O₃ and CuO dispersed in each. Experimental results show that the thermal conductivity of the mixtures is higher than that of the base fluids. However, theoretical models for predicting effective thermal conductivity of mixtures significantly underestimate the measured values, indicating their inadequacy for nanoparticle-fluid mixtures. The study discusses possible mechanisms for the enhanced thermal conductivity, including microscopic particle motion, particle structures, and surface properties. It also examines the effects of different dispersion techniques on the thermal conductivity of the mixtures. The results show that the thermal conductivity of the mixtures increases with decreasing particle size and varies depending on the dispersion method. Theoretical models for calculating effective thermal conductivity of mixtures are compared with experimental data. The models fail to accurately predict the measured thermal conductivity of the nanoparticle-fluid mixtures, suggesting that current models do not account for the complex energy transfer processes in such systems. The study highlights the need for a more comprehensive theory to explain the behavior of nanoparticle-fluid mixtures. The thermal conductivity of the mixtures is also found to be influenced by the viscosity of the fluid. The viscosity of the mixtures increases with the volume fraction of nanoparticles, which can affect the performance of the fluid in heat transfer applications. The study concludes that nanoparticle-fluid mixtures have potential for use in heat transfer applications, but further research is needed to fully understand their behavior and optimize their performance.