ArticleScientific reports2024
Experimental and explainable machine learning approach on thermal conductivity and viscosity of water based graphene oxide based mono and hybrid nanofluids.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Examining the experimental effects of concentration and temperature on the viscosity of nanofluid containing graphene oxide, suggesting a correlation, and developing a neural network.Scientific reports · 2026Article
- Optimizing drilling fluid rheology with hybrid nanoparticles boron nitride and graphene nanosheets: an experimental study.Scientific reports · 2026Article
- Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties.Scientific reports · 2025Article
- Predicting Thermal Conductivity of Nanoparticle-Doped Cutting Fluid Oils Using Feedforward Artificial Neural Networks (FFANN).Micromachines · 2025Article
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5 authors.
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Abstract
This study explores the thermal conductivity and viscosity of water-based nanofluids containing silicon dioxide, graphene oxide, titanium dioxide, and their hybrids across various concentrations (0 to 1 vol%) and temperatures (30 to 60 °C). The nanofluids, characterized using multiple methods, exhibited increased viscosity and thermal conductivity compared to water, with hybrid nanofluids showing superior performance. Graphene oxide nanofluids displayed the highest thermal conductivity and viscosity ratios, with increases of 52% and 177% at 60 °C and 30 °C, respectively, for a concentration of 1 vol% compared to base fluid. Similarly, graphene oxide-TiO
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