Influence of solvents on the enhancement of thermophysical properties and stability of multi-walled carbon nanotubes nanofluid

Influence of Emerging Semiconductive Nanoparticles on AC Dielectric Strength of Synthetic Ester Midel-7131 Insulating Oil

Exploring impressively effective dielectric nanofluids for transformers to improve dielectric strength and thermal stability is indispensable. It is crucial to determine the modification mechanism of dispersed nanomaterials in insulating oil for operative applications in power transformers. This paper aspires to authenticate the experimental evidence of the enhancing AC dielectric strength of synthetic ester Midel-7131 using two newly introduced semiconductive nanoparticles, CdS and Co3O4, and uncover the potential reasons for enhanced AC dielectric strength. The AC breakdown voltage (BDV) of synthetic ester and nanofluids was investigated and statistically evaluated. The mean AC breakdown voltage of SE/CdS and SE/Co3O4 was increased by 31.9% and 31.3%, respectively. The augmentation in AC breakdown strength is possibly due to the facilitated charge-scavenging ability owing to the large specific surface area and wide bandgap. Simultaneous thermogravimetric analysis, differential scanning calorimetry, and derivative thermogravimetry analyses (TGA-DSC-DTG) confirmed that the initial decomposition temperature was high and heat dissipation was low, indicating that the nanofluids were thermally stable in both air and nitrogen. Hence, emerging semiconductive CdS and Co3O4-based nanofluids of synthetic ester possess remarkable dielectric strength and thermal stability enhancement for their application in power transformers.

Strontium ranelate incorporated 3D porous sulfonated PEEK simulating MC3T3-E1 cell differentiation

Polyetheretherketone (PEEK) has been used as an implant material because it has similar mechanical properties to natural bone. However, inferior osseointegration and bioinertness hamper the clinical application of PEEK. In this study, the surfaces of sulfonated three-dimensional (3D) PEEK porous structures were loaded with different concentrations of strontium ranelate, a compound commonly used in the treatment or prevention of osteoporosis by promoting bone formation and inhibiting bone resorption. Field-emission scanning electron microscopy was used to characterize the topography of the structures, elemental carbon, oxygen and strontium contents were measured by X-ray photoelectron spectroscopy, and surface zeta potentials and water-contact angle were also measured. The results indicated that strontium ranelate was successfully loaded onto the 3D porous structures. In vitro cellular results showed that strontium ranelate-treated sulfonated PEEK (SP-SR) strengthened the adhesion of MC3T3-E1 cells. The activity of alkaline phosphatase, collagen secretion and extracellular matrix mineralization deposition of MC3T3-E1 cells were also improved on the surface of SP-SR. These results indicate that SP-SR could serve a new implant candidate for surgical treatment.

Optimization of Thermophysical and Rheological Properties of Mxene Ionanofluids for Hybrid Solar Photovoltaic/Thermal Systems

Since technology progresses, the need to optimize the thermal system's heat transfer efficiency is continuously confronted by researchers. A primary constraint in the production of heat transfer fluids needed for ultra-high performance was its intrinsic poor heat transfer properties. MXene, a novel 2D nanoparticle possessing fascinating properties has emerged recently as a potential heat dissipative solute in nanofluids. In this research, 2D MXenes (Ti3C2) are synthesized via chemical etching and blended with a binary solution containing Diethylene Glycol (DEG) and ionic liquid (IL) to formulate stable nanofluids at concentrations of 0.1, 0.2, 0.3 and 0.4 wt%. Furthermore, the effect of different temperatures on the studied liquid's thermophysical characteristics such as thermal conductivity, density, viscosity, specific heat capacity, thermal stability and the rheological property was experimentally conducted. A computational analysis was performed to evaluate the impact of ionic liquid-based 2D MXene nanofluid (Ti3C2/DEG+IL) in hybrid photovoltaic/thermal (PV/T) systems. A 3D numerical model is developed to evaluate the thermal efficiency, electrical efficiency, heat transfer coefficient, pumping power and temperature distribution. The simulations proved that the studied working fluid in the PV/T system results in an enhancement of thermal efficiency, electrical efficiency and heat transfer coefficient by 78.5%, 18.7% and 6%, respectively.

Carbon Nanomaterial-Based Nanofluids for Direct Thermal Solar Absorption

Recently, many scientists have been making remarkable efforts to enhance the efficiency of direct solar thermal absorption collectors that depends on working fluids. There are a number of heat transfer fluids being investigated and developed. Among these fluids, carbon nanomaterial-based nanofluids have become the candidates with the most potential by the heat absorbing and transfer properties of the carbon nanomaterials. This paper provides an overview of the current achievements in preparing and exploiting carbon nanomaterial-based nanofluids to direct thermal solar absorption. In addition, a brief discussion of challenges and recommendations for future work is presented.

An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids

Experimental data of thermal conductivity, thermal stability, specific heat capacity, viscosity, UV–vis (light transmittance) and FTIR (light absorption) of Multiwalled Carbon Nanotubes (MWCNTs) dispersed in glycols, alcohols and water with the addition of sodium dodecylbenzene sulfonate (SDBS) surfactant for 0.5 wt % concentration along a temperature range of 25 °C to 200 °C were verified using Artificial Neural Networks (ANNs). In this research, an ANN approach was proposed using experimental datasets to predict the relative thermophysical properties of the tested nanofluids in the available literature. Throughout the designed network, 65% and 25% of data points were comprehended in the training and testing set while the other 10% was utilized as a validation set. The parameters such as temperature, concentration, size and time were considered as inputs while the thermophysical properties were considered as outputs to develop ANN models of further predictions with unseen datasets. The results found to be satisfactory as the (coefficient of determination) R2 values are close to 1.0. The predicted results of the nanofluids’ thermophysical properties were then validated with experimental dataset values. The validation plots of all individual samples for all properties were graphically generated. A comparison study was conducted for the robustness of the proposed approach. This work may help to reduce the experimental time and cost in the future.