In recent years, observed focus greatly on gold nanoparticles synthesis due to its unique properties and tremendous applicability. In most of these researches, the citrate reduction method has been adopted. The aim of this study was to prepare and optimize monodisperse ultrafine particles by addition of reducing agent to gold salt, as a result of seed mediated growth mechanism. In this research, gold nanoparticles suspension (G) was prepared by traditional standard Turkevich method and optimized by studying different variables such as reactants concentrations, preparation temperature and stirring rate on controlling size and uniformity of nanoparticles through preparing twenty formulas (G1-G20). Subsequently, the selected formula that pr

It was aimed to investigate the compressibility, compactibility, powder flow and tablet disintegration of a new excipient comprising magnesium (Mg) silicate co-processed (5%–85% w/w) onto chitin, microcrystalline cellulose (MCC) and starch as the hydrophilic polymers of interest. Initially, the mechanism of tablet disintegration was studied by measuring water infiltration rate, moisture sorption, swelling capacity and hydration ability. Moreover, the powders compression behavior was carried out by applying Kawakita model of compression analysis in addition to porosity and radial tensile strength measurements. In vitro drug release of compacts made of 400 mg ibuprofen and 300 mg of the hydrophilic polymers containing 30% w/w Mg silicat

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Abstract

This research aims to design a multi-objective mathematical model to assess the project quality based on three criteria: time, cost and performance. This model has been applied in one of the major projects formations of the Saad Public Company which enables to completion the project on time at an additional cost that would be within the estimated budget with a satisfactory level of the performance which match with consumer requirements. The problem of research is to ensure that the project is completed with the required quality Is subject to constraints, such as time, cost and performance, so this requires prioritizing multiple goals. The project

The division partitioning technique has been used to analyze the four electron systems into six-pairs electronic wave functions for ( for the Beryllium atom in its excited state (1s2 2s 3s ) and like ions ( B+1 ,C+2 ) using Hartree-Fock wave functions . The aim of this work is to study atomic scattering form factor f(s) for and nuclear magnetic shielding constant. The results are obtained numerically by using the computer software (Mathcad).

Coupling reaction of m-and p- amino acetop henone and p-amino benzoic acid with (LHistidine) gave the new bidentate azo ligands (L1, L2 and L3). The prepared ligands were identified by FT-IR, UV-Vis, 1HNMR and GC- mass sp ectroscopic technique. Treatment of the prepared ligands with the following metal ions (CoII, NiII, CuII, ZnII, CdII and HgII) in aqueous ethanol with a 1:2 M:L ratio and at optimum pH, yielded a series of neutral complexes of the general formula [M (L)2 Cl2]. The prepared complexes were characterized by using flame atomic absorption, FT-IR, UV-Vis and 1HNMR spectroscopic methods as well as magnetic susceptibility and conductivity measurements. Chloride ion content was also evaluated by (Mohr method). The nature of the com

In the present work, Response Surface Methodology (RSM) was utilized to optimize process variables and find the best circumstances for indirect electrochemical oxidation of mimicked wastewater to remove phenol contaminants using prepared ternary composite electrode. The electrodeposition process is used for the synthesis of a ternary composite electrode of Mn, Co, and Ni oxides. The selected concentrations of metal salts of these elements were 0.05, 0.1, and 1.5 M, with constant molar ratio, current density, and electrolysis time of 1:1:1, 25 mA/cm2, and 2 h. Interestedly, the gathered Mn-Co-Ni oxides were deposited at both the anode and cathode. X-ray diffraction (XRD) and scanning electron microscopy (SEM) facilitated the qualitative char

When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat