Abstract: Chalcones were used to synthesis series of 2-pyrazoline derivatives and evaluated their antimicrobial and anti-inflammatory activities (E)-1,3-diphenylprop-2-en-1-one (1-5) were synthesized by Claisen-Schmidt Condensation method through the reaction of acetophenone with five various para substituted benzaldehyde in presence of KOH, the reaction monitoring by TLC and the result intermediates were checked by melting point and FT-IR Various 2-Pyrazoline derivatives were prepared by one pot reaction that involved the refluxing of (E)-1,3-diphenylprop-2-en-1-one (1–5) and Hydrazine monohydrate in the presence of glacial acetic acid for 24 hours at a temperature of (45–50) °C for a duration 24 hrs. pyrazoline II (1-5) was formed, then antioxidant compound (guaiacol) was added reflex for 24-48 hours, monitoring by TLC to ensure the reaction was completed. The final mixture was cooled in ice-water bathe until the formation of crystals were completed, added to crushed ice, kept in refrigerator overnight, filter, recrystallized and then dried to get 2-pyrazoline derivatives, IIIa, IIIb, IIIc, IIId and, IIIe. based on the spectral data 2-pyrazoline derivatives structures have been confirmed. The synthesized compounds were screened for their antimicrobial activity and anti-inflammatory.
A thin film of (SnSe) and SnSe:Cu with various Cu ratio (0,3,5 and 7)% have been prepared by thermal evaporation technique with thickness 400±20 nm on glass substrate at (R.T). The effect of Cu dopants concentration on the structural, morphological, optical and electrical properties of (SnSe) Nano crystalline thin films was explored by using X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), energy dispersive spectroscopy (EDS), UV–Vis absorption spectroscopy and Hall Effect measurement respectively. X-ray diffraction analysis reveal the polycrystalline nature of the all films deposited with orthorhombic structure which possess a preferred orientation along the (111) plane. The crystalline sizes o
... Show MoreAbstract: The development of highly sensitive sensors has become an efficient field of research. In this work, an ArF Excimer laser of 193 nm with a maximum pulse energy of 275 mJ, 15 ns pulse duration and a repetition rate of 1 Hz is utilized to form a Laser Induced Periodic Surface Structures (LIPSS) of three different morphologies (nanochains, contours, grooves) on surface of CR39 polymer at a fluence range above the ablation threshold (250 mJ/cm2). The laser ablated polymer surface is then Surface Enhanced Raman Scattering (SERS) activated by deposition of a gold layer of 30 nm thickness. The capability of the produced substrate for surface enhanced Raman scattering is evaluated through thiophenol as an analyte molecule. It is observ
... Show MoreIn this paper had been studied the characterization of the nanocatalyst (NiO) Mesh electrodes. For fuel cell. The catalyst is prepared and also the electrodes The structural were studied through the analysis of X-ray diffraction of the prepared nanocatalyst for determining the yielding phase and atomic force microscope to identify the roughness of prepared catalyst surface, Use has been nanocatalyst led to optimization of cell voltage, current densities & power for a fuel cell.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
... Show MoreA novel technique Sumudu transform Adomian decomposition method (STADM), is employed to handle some kinds of nonlinear time-fractional equations. We demonstrate that this method finds the solution without discretization or restrictive assumptions. This method is efficient, simple to implement, and produces good results. The fractional derivative is described in the Caputo sense. The solutions are obtained using STADM, and the results show that the suggested technique is valid and applicable and provides a more refined convergent series solution. The MATLAB software carried out all the computations and graphics. Moreover, a graphical representation was made for the solution of some examples. For integer and fractional order problems, solutio
... Show MoreAbstract. This study presents experimental and numerical investigation on the effectiveness of electrode geometry on flushing and debris removal in Electrical Discharge Drilling (EDD) process. A new electrode geometry, namely side-cut electrode, was designed and manufactured based on circular electrode geometry. Several drilling operations were performed on stainless steel 304 using rotary tubular electrodes with circular and side-cut geometries. Drilling performance was characterized by Material Removal Rate (MRR), Electrode Wear Rate (EWR), and Tool Wear Ratio (TWR). Dimensional features and surface quality of drilled holes were evaluated based on Overcut (OC), Hole Depth (HD), and Surface Roughness (SR). Three-dimensional
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
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