Complexes of Lanthanide ione Ln(III) =La(III) , Ce(III),Pr(III) and Nd(III) withligands of nicotinamide (na) and Benzimidazole (BIMD) have been prepared withgeneral formula [M(na)3(BIMD)3](NO3) where :M = Ln(III) = La(III) , Ce(III) , Gd(III) , Nd(III) .Na = nicotinamide = C7H6N2OBIMD = Benzimidazole = C7H6N2All compounds have been characterized by spectroscopic methods [FT-IR , UV-VIS ,AAS] , microanalysis (C.H.N) Along with conductivity measurements , solubility ,melting point , theroitical measurment by using chem office 3D prog .Model (2000) .Frome the above data the proposed moleculer structure for all complexes with its ionsis octahydral geometries

Nine new compounds of 2-amino-5-chlorobenzothiazole derivatives were synthesized. These new compounds were formed through the reaction of 2-amino-5-chlorobenzothiazole 1 with ethyl chloroacetate and KOH, which gave an ester derivative 2, followed by refluxing compound 2 with hydrazine hydrate to afford hydrazide derivative 3. The reaction of compound 3 with CS₂ and KOH gave 1,3,4-oxadiazole-2-thiol derivative 4, and then the reaction of compound 2 with thiosemicarbazide to produce compound 5  then treated it with 4%NaOH led to ring closure to provide 1,2,4-triazole-3-thiol derivative

BCl3 is toxic gas and its detection is of great importance. Thus, here, B3LYP, M06-2X, and B97D density functionals are utilized for probing the effect of decorating Zn, Cd, and Au on the sensing performance of an AlP nano-sheet (AlPNS) in detecting the BCl3. We predict that the interaction of pure AlPNS with BCl3 is physisorption, and the sensing response (SR) of AlPNS is approximately 9.2. The adsorption energy of BCl3 changes from −4.1 to −18.8, −19.1, and −19.5 kcal/mol by decorating the Zn, Cd, and Au metals into the AlPNS surface, respectively. Also, the corresponding SR meaningfully rises to 40.4, 59.0, and 80.9, indicating that by increasing the atomic number of metals, the sensitivity of metal decorated AlPNS (metal@AlPNS)

The topological parameters of the metal-metal and metal-ligand bonding interactions in a trinuclear tetrahydrido cluster [(Cp*Co) (CpRu)2 (μ3-H) (μ-H)3]1 (Cp* = η5 -C5Me4Et), (Cp = η5 -C5Me5), was explored by using the Quantum Theory of Atoms-in-Molecules (QTAIM). The properties of bond critical points such as the bond delocalization indices δ (A, B), the electron density ρ(r), the local kinetic energy density G(r), the Laplacian of the electron density ∇2ρ(r), the local energy density H(r), the local potential energy density V(r) and ellipticity ε(r) are compared with data from earlier organometallic system studies. A comparison of the topological processes of different atom-atom interactions has become possible than

The permeable reactive barrier (PRB) is one of the promising innovative in situ groundwater remediation technologies, in removing of copper from a contaminated shallow aquifer. The 1:1- mixture of waste foundry sand (WFS) and Kerbala’s sand (KS) was used for PRB. The WFS was represented the reactivity material while KS used to increase the permeability of PRB only. However, Fourier-transform infrared (FTIR) analysis proved that the carboxylic and alkyl halides groups are responsible for the sorption of copper onto WFS. Batch tests have been performed to characterize the equilibrium sorption properties of the (WFS+KS) mix in copper- containing aqueous
solutions. The sorption data for Cu+2 ions, obtained by batch experiments, have be

FH Ghanim, Journal of Global Pharma Technology, 2018

In 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