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<p>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&#xa0;linear decrease with temperature. Surface tension measurements reveal a&#xa0;consistent reduction compared to pure ethylene glycol, aligning with a&#xa0;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&#xa0;K were determined to be 47.906 mN&#xa0;<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10765_2025_3553_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="35" /> </InlineMediaObject> <EquationSource Format="TEX">\(\cdot {\rm m}^{-1}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>·</mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation>. A key contribution of this work is the introduction of the concept of a&#xa0;material data table for nanofluids, which aims to consolidate fragmented experimental data into a&#xa0;standardized framework. Such a&#xa0;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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年5月10日

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  • Julian Traciak,
  • Krzysztof Koziol,
  • Magdalena Małecka,
  • Anna Blacha,
  • Sławomir Boncel,
  • Gaweł Żyła

摘要

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  \(\cdot {\rm m}^{-1}\) · 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.