The First Step into Material Table Dataset for Surface Tension of Nanofluids: Insights from the Case Study of Ethylene Glycol-Based Graphene Nanofluids

Abstract

This study investigates the density and surface tension properties of graphene flake-ethylene glycol (GF-EG) nanofluids. The experimental results demonstrate that the density of GF-EG nanofluids increases with nanoparticle mass fractions while exhibiting a linear decrease with temperature. Surface tension measurements reveal a consistent reduction compared to pure ethylene glycol, aligning with a previously established model that attributes this behavior to nanoparticle saturation at the fluid surface. Notably, the averaged surface tension values for GF-EG nanofluids at 298.15 K were determined to be 47.906 mN \(\cdot {\rm m}^{-1}\). A key contribution of this work is the introduction of the concept of a material data table for nanofluids, which aims to consolidate fragmented experimental data into a standardized framework. Such a dataset would enable more accurate prediction of surface tension behavior in different nanofluid systems and facilitate advances in artificial intelligence-based modeling that can identify correlations between nanoparticle characteristics and surface tension, enabling rapid optimization of nanofluids for specific applications. This study not only provides new insights into GF-EG nanofluids in terms of surface tension, but also highlights the transformative potential of artificial intelligence in accelerating the discovery and implementation of next-generation heat transfer fluids.