4.6 Article

Synthesis and characterization of additive graphene oxide nanoparticles dispersed in water: Experimental and theoretical viscosity prediction of non-Newtonian nanofluid

Journal

Publisher

WILEY
DOI: 10.1002/mma.6381

Keywords

ANN; correlation; graphene oxide; rheological behavior; viscosity

Funding

  1. National Natural Science Foundation of China [51979261]
  2. Australia Research Council [DE190100931]
  3. Natural Science Foundation of Fujian Province [2018J01506]
  4. Tai'shan Scholar Fund of Shandong Province of China [tsqn201812025]
  5. Australian Research Council [DE190100931] Funding Source: Australian Research Council

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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 degrees C, 30 degrees C, 35 degrees C, 40 degrees C, 45 degrees C, and 50 degrees C; and several shear ranges: 12.23, 24.46, 36.69, 61.15, 73.38, and 122.3 s(-1) 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 R-2 = 0.99. It can be concluded that GO can be used in thermal systems as stable nanofluid with agreeable viscosity.

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