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<p>This study presents comparative evaluation of thermal conductivity (TC) and dynamic viscosity enhancement of high-oleic soybean oils (HOSO) by dispersing five nanoparticles—two graphene nanoplatelets (xGnP, GnP), MoS<sub>2</sub>, TiO<sub>2</sub>, and Al<sub>2</sub>O<sub>3</sub>—to formulate high performance nanofluids for minimum quantity lubrication (MQL) machining. TC was measured for 1–7 wt.% nanoparticle concentrations over 25–75&#xa0;°C (10&#xa0;°C increments) using a Transient Hot Wire meter, and viscosity was measured for 1–4 wt.% at 25, 45, and 75 °C using a ViscoQC 300 rotational viscometer. Ultrasonic dispersion ensured uniform nanoparticle distribution. Results show that TC decreases linearly with temperature but increases nonlinearly with nanoparticle wt.% concentration. Viscosity decreases exponentially with increasing shear rate and temperature but rises nonlinearly with nanoparticle wt.% concentration. The highest TC enhancement at 7 wt.% occurred with xGnP/HOSO nanofluids (~ 214% at 25&#xa0;°C, ~ 196% at 75&#xa0;°C) followed by GnP/HOSO (~ 37%, ~ 21%), MoS<sub>2</sub>/HOSO (~ 16%, ~ 9%), TiO<sub>2</sub>/HOSO (~ 12%, 10%), and Al<sub>2</sub>O<sub>3</sub>/HOSO (~ 12%, ~ 7%). xGnP/HOSO achieved TC values of 0.53 W m<sup>−1</sup> K<sup>−1</sup> (25 °C) and 0.47 W m<sup>−1</sup> K<sup>−1</sup> (75&#xa0;°C), approaching conventional emulsion coolants (CEC) levels [0.62, 0.66 W m<sup>−1</sup> K<sup>−1</sup>]. At 4 wt.%, xGnP/HOSO showed the highest viscosity enhancement (622% at 25&#xa0;°C, 784% at 75&#xa0;°C), followed by TiO<sub>2</sub>/HOSO (18.6%; 29.4%), MoS<sub>2</sub>/HOSO (12.3, 9.9%), GnP/HOSO (11.6, 5.9%), Al<sub>2</sub>O<sub>3</sub>/HOSO (8.8, 3.7%). Nanofluid formulation showed that xGnP concentrations above 5 wt.% reduce flowability. xGnP showed superior enhancement of TC and viscosity compared to MoS<sub>2</sub> and metal oxides. xGnP/HOSO at 5 wt.% are recommended as sustainable CEC substitute for nanofluid-based MQL machining.</p>

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Thermal conductivity and viscosity characterization of soybean oil nanofluids dispersed with graphene nanoplatelets, MoS2, TiO2 and Al2O3 nanoparticles for MQL machining

  • 期刊论文

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  • 发表日期：

    2026年6月30日

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  • Anthony Chukwujekwu Okafor,
  • Saidanvar Esanjonovich Valiev,
  • Tobechukwu Kingsley Abor,
  • Ignatius Echezona Ekengwu,
  • Abiodun Saka,
  • Monday U. Okoronkwo

摘要

This study presents comparative evaluation of thermal conductivity (TC) and dynamic viscosity enhancement of high-oleic soybean oils (HOSO) by dispersing five nanoparticles—two graphene nanoplatelets (xGnP, GnP), MoS2, TiO2, and Al2O3—to formulate high performance nanofluids for minimum quantity lubrication (MQL) machining. TC was measured for 1–7 wt.% nanoparticle concentrations over 25–75 °C (10 °C increments) using a Transient Hot Wire meter, and viscosity was measured for 1–4 wt.% at 25, 45, and 75 °C using a ViscoQC 300 rotational viscometer. Ultrasonic dispersion ensured uniform nanoparticle distribution. Results show that TC decreases linearly with temperature but increases nonlinearly with nanoparticle wt.% concentration. Viscosity decreases exponentially with increasing shear rate and temperature but rises nonlinearly with nanoparticle wt.% concentration. The highest TC enhancement at 7 wt.% occurred with xGnP/HOSO nanofluids (~ 214% at 25 °C, ~ 196% at 75 °C) followed by GnP/HOSO (~ 37%, ~ 21%), MoS2/HOSO (~ 16%, ~ 9%), TiO2/HOSO (~ 12%, 10%), and Al2O3/HOSO (~ 12%, ~ 7%). xGnP/HOSO achieved TC values of 0.53 W m−1 K−1 (25 °C) and 0.47 W m−1 K−1 (75 °C), approaching conventional emulsion coolants (CEC) levels [0.62, 0.66 W m−1 K−1]. At 4 wt.%, xGnP/HOSO showed the highest viscosity enhancement (622% at 25 °C, 784% at 75 °C), followed by TiO2/HOSO (18.6%; 29.4%), MoS2/HOSO (12.3, 9.9%), GnP/HOSO (11.6, 5.9%), Al2O3/HOSO (8.8, 3.7%). Nanofluid formulation showed that xGnP concentrations above 5 wt.% reduce flowability. xGnP showed superior enhancement of TC and viscosity compared to MoS2 and metal oxides. xGnP/HOSO at 5 wt.% are recommended as sustainable CEC substitute for nanofluid-based MQL machining.