Thermal Conductivity Enhancement via Synthesis Produces a New Hybrid Mixture Composed of Copper Oxide and Multi-walled Carbon Nanotube Dispersed in Water: Experimental Characterization and Artificial Neural Network Modeling

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Nanofluid is a solid–fluid mixture. By using one solid nanoparticle or one fluid, mono-nanofluid (MN) forms, and by using two solid nanoparticles (NPs) or two fluids, hybrid-nanofluid (HN) forms. For this study, for MN, copper oxide (CuO) and for HN, two solids, which are CuO and multi-walled carbon nanotube (MWCNT) were dispersed in base fluid which is water. After nanofluid preparation, thermal conductivity was measured, and the achievements were numerically modeled. After that, XRD–EDX were performed for the phase-structural analysis. Then, FESEM was examined for NPs-microstructural study. Thermal conductivity (TC) of MN and HN were investigated at 0.2 % to 1.0 % volume fractions (Vf) in 25 °C to 50 °C temperature (T) ranges. Thermal conductivity enhancements of 19.16 % and 37.05 % were seen at the utmost Vf and T for mono-nanofluid and hybrid-nanofluid, respectively. New correlations have been presented with R2 = 0.9, and also Artificial Neural Network (ANN) has been done with R2 = 0.999. For the presented correlation, 0.86 %, and 0.51 % deviations, and for the trained model, 0.41 % and 0.51 % deviations were estimated for mono-nanofluid and hybrid-nanofluid, respectively. As a final result, by adding MWCNT to CuO–H2O mixture, thermal conductivity is raised by 17.89 %, and the hybrid-nanofluid has acceptable heat-transfer capability

Synthesis of new dihybrid nanofluid of TiO2/MWCNT in water–ethylene glycol to improve mixture thermal performance: preparation, characterization, and a novel correlation via ANN based on orthogonal distance regression algorithm

Nanofuid refers to the mixture of fuid and solid nanoparticles. If this mixture contains more than one NP or fuid, it is called “hybrid nanofuid”; further, if HN contains more than one NP and also more than one fuid, it is called “dihybrid nanofuid.” In this research, frst, titanium dioxide NP was dispersed in the water–ethylene glycol basefuid and formed an HN. Then, thermal conductivity of HN was measured. After that, MWCNT NP was added to the HN and formed a DHN. Further, TC of DHN measured. Both HN and DHN TCs were compared, and the results revealed that by adding MWCNT, thermal conductivity enhanced about 30.83% (from 25.65% of HN to 56.48% of DHN). On the other hand, to analyze the phase structure, and to observe the microstructure, X-ray difraction analysis, energy-dispersive X-ray analysis, and feld emission scanning electron microscopy were examined. The measured TC for both samples was at volume fractions up to 1.0% and temperatures up to 50 °C. After an experimental study, two novel correlations were calculated by the curve-ftting method for HN and DHN, individually. In the end, to predict the other Vf and temperature, an artifcial neural network has been modeled for both HN and DHN

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

© 2020 John Wiley & Sons, Ltd. 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\u27s 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−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\u27s viscosity, a correlation presented 1.88% (for RPM = 10) and 0.56% (for RPM = 100) deviation. Finally, to predict nanofluid\u27s viscosity in other mass fractions and temperatures, an artificial neural network has been modeled by R2 = 0.99. It can be concluded that GO can be used in thermal systems as stable nanofluid with agreeable viscosity

Evaluation of the effects of pycnogenol (French maritime pine bark extract) supplementation on inflammatory biomarkers and nutritional and clinical status in traumatic brain injury patients in an intensive care unit: a randomized clinical trial protocol

Background: Traumatic brain injury (TBI) is one of the major health and socioeconomic problems in the world. Immune-enhancing enteral formula has been proven to significantly reduce infection rate in TBI patients. One of the ingredients that can be used in immunonutrition formulas to reduce inflammation and oxidative stress is pycnogenol. Objective: The objective of this work is to survey the effect of pycnogenol on the clinical, nutritional, and inflammatory status of TBI patients. Methods: This is a double-blind, randomized controlled trial. Block randomization will be used. An intervention group will receive pycnogenol supplementation of 150 mg for 10 days and a control group will receive a placebo for the same duration. Inflammatory status (IL-6, IL- 1β, C-reactive protein) and oxidative stress status (malondialdehyde, total antioxidant capacity), at the baseline, at the 5th day, and at the end of the study (10th day) will be measured. Clinical and nutritional status will be assessed three times during the intervention. The Sequential Organ Failure Assessment (SOFA) questionnaire for assessment of organ failure will be filled out every other day. The mortality rate will be calculated within 28 days of the start of the intervention. Weight, body mass index, and body composition will be measured. All analyses will be conducted by an initially assigned study arm in an intention-to-treat analysis. Discussion: We expect that supplementation of 150 mg pycnogenol for 10 days will improve clinical and nutritional status and reduce the inflammation and oxidative stress of the TBI patients.&nbsp