The preparation, spectroscopic characterisation of complexes derived from the mixed ligands with CdII, ZnII and CoII metal ions with Schiff base, Dithiocarbamates (DTCs) and 8-Hydroxyquinoline are reported. The compounds that prepared have been defined via; chloride content, F.T-IR, UV-Vis 1H-NMR spectroscopy and C.H.N.S, as well as conductance and magnetic susceptibility.All data which collected from such methods specified complexes with 6 coordinates in solution and solid states. The biologicalactivity that is related to all the prepared compounds which were screened for their antimicrobial activitiesagainst (G+ and (G- )). The data that collected from biological activity indicate that complexes will have extra activity against such teste

The 3-aminoacetophenone and 4-aminoantipyrine were used as ‎precursors to prepare new six ligands. The three new ligands (L1,L2 ‎and L3) were synthesis by reacting one mole of 3-aminoacetophenone ‎with one mole of (Acetyl chloride), (benzoyl chloride), (4-‎methoxybenzoyl chloride) and ammonium thiocyanat in acetone as a ‎solvent, they are:-‎ L1 (AAA) =[N-(3-acetylphenylcarbamothioyl)acetamide]‎ L2 (BAA) =[N-(3-acetylphenylcarbamothioyl)benzamide]‎ L3 (MAA) =[N-(3-acetylphenylcarbamothioyl)-4-methoxy benzamide]‎ Also three new derivatives of 4-aminoantipyrine were synthesis by ‎reacting one mole of 4-aminoantipyrine with one mole of (Acetyl ‎chloride), (benzoyl chloride), (4-methoxybenzoyl chloride) and ‎ammonium thio

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

Bacteria could produce bacterial nanocellulose through a procedure steps: polymerization and crystallization, that occur in the cytoplasm of the bacteria, the residues of glucose polymerize to (β-1,4) lineal glucan chains that produced from bacterial cell extracellularly, these lineal glucan are converted to microfbrils, after that these microfbrils collected together to shape very pure three dimensional pored net. It could be obtained a pure cellulose that created by some M.O, from the one of the active producer organism like Acetic acid bacteria (AAB), that it is a gram -ve, motile and live in aerobic condition. The bacterial nanocellulose (BNC) have great consideration in many fields because of its flexible properties, features