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Formulation details for hybrid GNP–Ag nanofluids.

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Figshare2025-11-04 更新2026-04-28 收录
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https://figshare.com/articles/dataset/Formulation_details_for_hybrid_GNP_Ag_nanofluids_/30533804
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This study examines the thermophysical properties of ethylene glycol–glycerol (60:40 v/v) hybrid nanofluids containing graphene nanoplatelets (GNPs) and silver nanoparticles (Ag) at concentrations of 0.1–0.5 vol.%. The nanofluids were synthesized using a two-step method with Tween-80 surfactant to enhance dispersion stability. High-resolution transmission electron microscopy (TEM) and Raman spectroscopy confirmed the morphology, lateral size, few-layer structure of GNPs, and the attachment of Ag nanoparticles. The addition of surfactant increased the zeta potential from 15.7 mV to 35.2 mV for the 0.1 vol.% GNPs/Ag formulation, indicating a marked improvement in colloidal stability. Thermal conductivity enhancement reached 102.85% at 0.1 vol.% with only a 19.84% viscosity increase. Higher nanoparticle loadings improved conductivity further but caused significant viscosity increases and reduced stability. Specific heat capacity decreased by up to 46.45%, potentially benefiting rapid thermal response applications but limiting heat storage capacity. Comparison with recent literature showed that the present formulation outperforms several similar Ag- and GNP-based nanofluids in thermal conductivity enhancement while maintaining manageable viscosity. This study is the first to report such high conductivity improvement in an EG–GLY-based hybrid nanofluid at ultra-low loading, achieved through optimized surfactant use, validated structural characterization, and benchmarking against literature. Low-concentration GNPs/Ag hybrid nanofluids, particularly at 0.1 vol.%, offer strong potential for thermal management applications where high heat transfer performance and acceptable pumping requirements are critical. However, stability limitations at higher concentrations and viscosity–conductivity trade-offs highlight the need for further optimization before large-scale deployment.
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2025-11-04
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