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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Malekahmadi, Omid
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Publications (2/2 displayed)
- 2020Synthesis and characterization of additive graphene oxide nanoparticles dispersed in water: Experimental and theoretical viscosity prediction of non‐Newtonian nanofluidcitations
- 2020Thermal conductivity enhancement of nanofluid by adding multiwalled carbon nanotubes: Characterization and numerical modeling patternscitations
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article
Synthesis and characterization of additive graphene oxide nanoparticles dispersed in water: Experimental and theoretical viscosity prediction of non‐Newtonian nanofluid
Abstract
<jats:p>Graphene oxide (GO) is a mixture of carbon, oxygen, and hydrogen. GO sheets used to make tough composite materials, thin films, and membranes. GO‐water nanofluid's rheological behavior was investigated in this research. Various mass fractions: 1.0, 1.5, 2.0, 2.5, and 3.5 mg/ml; different temperature ranges: 25°C, 30°C, 35°C, 40°C, 45°C, and 50°C; and several shear ranges: 12.23, 24.46, 36.69, 61.15, 73.38, and 122.3 s<jats:sup>−1</jats:sup> were studied. X‐ray diffraction analysis (XRD), energy dispersive X‐ray analysis (EDX), dynamic light scattering analysis (DLS), and Fourier‐transform infrared (FTIR) tests studied to analyze phase and structure. Field emission scanning electron microscope (FESEM) and transmission electron microscopy (TEM) tests studied for microstructural observation. The stability of nanofluid was checked by the zeta‐potential test. Non‐Newtonian behavior of nanofluid, similar to power‐law model (with power less than one), revealed by results. Also, results showed that viscosity increased by increment of mass fraction, and on the contrary, increment of temperature, caused a decrease in viscosity. Then, to calculate nanofluid's viscosity, a correlation presented 1.88% (for RPM = 10) and 0.56% (for RPM = 100) deviation. Finally, to predict nanofluid's viscosity in other mass fractions and temperatures, an artificial neural network has been modeled by <jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.99. It can be concluded that GO can be used in thermal systems as stable nanofluid with agreeable viscosity.</jats:p>