A pioneering idea for increasing the thermal performance of heat transfer fluids was to use ultrafine solid particles suspended in the base fluid. Nanofluids, synthesized by mixing solid nanometer sized particles at low concentrations with the base fluid, were used as a new heat transfer fluid and developed a remarkable effect on the thermophysical properties and heat transfer coefficient. For any nanofluid to be usable in heat transfer applications, the main concern is its long-term stability. The aim of this research is to investigate the effect of using four different surfactants (sodium dodecyl benzene sulfonate (SDBS), sodium dodecyl sulfate (SDS), cetyl trimethylammonium bromide (CTAB), and gum Arabic (GA)), each with three different concentrations, and five ultrasonication times (15, 30, 60, 90, and 120 min) on the stability of water-based graphene nanoplatelets (GNPs) nanofluids. In addition, the viscosity and thermal conductivity of the highest stability samples were measured at different temperatures. For this aim, nineteen different nanofluids with 0.1 wt% concentration of GNPs were prepared via the two-step method. An ultrasonication probe was utilized to disperse the GNPs in distilled water. UV–vis spectrometry, zeta potential, average particle size, and Transmission Electron Microscopy (TEM) were helpful in evaluating the stability and characterizing the prepared nanofluids. TEM and zeta potential results were in agreement with the UV–vis measurements. The highest nanofluid stability was obtained at 60-min ultrasonication time. The prepared water-based pristine GNPs nanofluids were not stable, and the stability was improved with the addition of surfactants. The presence of SDBS, SDS, and CTAB surfactants in the nanofluids resulted in excessive foam. The best water-based GNPs nanofluid was selected in terms of better stability, higher thermal conductivity, and lower viscosity. From all the samples that were prepared in this research, the (1–1) SDBS–GNPs sample with 60-min ultrasonication showed the highest stability (82% relative concentration after 60 days), the second better enhancement in the thermal conductivity of the base fluid (8.36%), and nearly the lowest viscosity (7.4% higher than distilled water).
1,3,4-oxadizole and pyrazole derivatives are very important scaffolds for medicinal chemistry. A literature survey revealed that they possess a wide spectrum of biological activities including anti-inflammatory and antitumor effects.
To describe the synthesis and evaluation of two classes of new niflumic acid (NF) derivatives, the 1,3,4-oxadizole derivatives (compounds 3 and (4A-E) and pyrazole derivatives (compounds 5 and 6), as EGFR tyrosine kinase inhibitors in silico and in vitro.
The designed compounds were synthesized using convent
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
This work includes the synthesis of new ester compounds containing two 1,3,4-oxadiazole rings, 15a-c and 16a-c. This was done over seven steps, starting with p-acetamido-phenol 1 and 2-mercaptobenzoimidazole 2. The structure of the products was determined using FT-IR, 1H NMR, and mass spectroscopy. The evaluation of the antimicrobial activities of some prepared compounds was achieved against four types of bacteria (two types of gram-positive bacteria; Staphylococcus aureus and Bacillus subtilis, and two types of gram-negative bacteria, Pseudomonas aeruginosa and E. Coli), as well as against one types of fungus (C. albino). The results show moderate activit against the study bacteria, and the theoretical analysis of the toxi
... Show MoreAn improved Metal Solar Wall (MSW) with integrated thermal energy storage is presented in this research. The proposed MSW makes use of two, combined, enhanced heat transfer methods. One of the methods is characterized by filling the tested ducts with a commercially available copper Wired Inserts (WI), while the other one uses dimpled or sinusoidal shaped duct walls instead of plane walls. Ducts having square or semi-circular cross sectional areas are tested in this work.
A developed numerical model for simulating the transported thermal energy in MSW is solved by finite difference method. The model is described by system of three governing energy equations. An experimental test rig has been built and six new duct configurations have b
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
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