Abstract
In last decade, Graphene Oxide is widely used in flexible rechargeable battery electrode, Graphene oxide lens, energy conversion, and Hydrogen storage. In following research, preparation, thermal conductivity (TC) measurement, stability, and modeling studied for Graphene Oxide-Water nanofluid synthesized through Modified hummers method. Furthermore, X-ray diffraction analysis (XRD), dynamic light scattering analysis (DLS), Fourier transform infrared (FTIR), field emission scanning electron microscope plus energy dispersive X-ray analysis (FESEM-EDX) and transmission electron microscopy (TEM) tests were used to study Microstructural-observation, Phase and structural analysis of nanoparticles. Also, the nanofluid stability was investigated using the Ultraviolet–visible spectroscopy analysis (UV–Vis), Zeta-potential and differential scanning calorimetry plus thermo gravimetric analysis (DSC-TG) tests. Nanofluid TC measurement was done in temperature and mass fraction ranges of 25–50 °C and 1.0–4.5 mg/ml. Stability results showed that nanofluid stability is >3 months and also 1.0 mg/ml nanofluid can work in applications with operational range up to 1000 °C. Results indicated utmost thermal conductivity enhancement (TCE) of 25.27%, which was in 4.5 mg/ml mass fraction at 50 °C temperature. Although, new correlation including 1.01% utmost deviation in order to compute nanofluid's TC, has been offered. Moreover, Recursive Least Squares Fuzzy model has been applied with R2 = 0.99. In the end, as reported by the results, it can be declared that GO nanoparticles synthesized by MH method, can be proposed as stable nanofluid with acceptable heat transfer potential in thermal systems.
Author supplied keywords
Cite
CITATION STYLE
Liu, W. I., Malekahmadi, O., Bagherzadeh, S. A., Ghashang, M., Karimipour, A., Hasani, S., … Goodarzi, M. (2019). A novel comprehensive experimental study concerned graphene oxide nanoparticles dispersed in water: Synthesise, characterisation, thermal conductivity measurement and present a new approach of RLSF neural network. International Communications in Heat and Mass Transfer, 109. https://doi.org/10.1016/j.icheatmasstransfer.2019.104333
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.