Coupling reaction of 2-amino benzoic acid with phenol gave the new bidentate azo ligand. The prepared ligand was identified by Microelemental Analysis, FT-IR and UV-Vis spectroscopic technique. Treatment of the prepared ligand with the following metal ions (CoII, NiII, CuII and ZnII) 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]. The prepared complexes were characterized using flame atomic absorption, (C.H.N) Analysis, FT-IR and UV-Vis spectroscopic methods as well as magnetic susceptibility and conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods, Beer's law obeyed over a concentr

ZG Abdulrazaq, MR Younus, Nasaq, 2023

Coupling reaction of 4-amino antipyrene with 2,6-dimethyl phenol gave bidentate azo ligand. The prepared ligand was identified by Microelemental Analysis, 1HNMR, FT-IR and UV-Vis spectroscopic techniques. Treatment of the prepared ligand 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)2Cl2]. The prepared complexes were characterized using flame atomic absorption, (C.H.N) Analysis, FT-IR and UVVis spectroscopic methods as well as magnetic susceptibility and conductivity measurements. Chloride ion content was also evaluated by (Mohr method). The nature of the complexes formed were studied followin

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images. So, this study aimed at testing the system performance at poo

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images.

So, this study aimed at testing the system performance at poor s

This study used a continuous photo-Fenton-like method to remediate textile effluent containing azo dyes especially direct blue 15 dye (DB15). A Eucalyptus leaf extract was used to create iron/copper nanoparticles supported on bentonite for use as catalysts (E@B-Fe/Cu-NPs). Two fixed-bed configurations were studied and compared. The first one involved mixing granular bentonite with E@B-Fe/Cu-NPs (GB- E@B-Fe/Cu-NPs), and the other examined the mixing of E@B-Fe/Cu-NPs with glass beads (glass beads-E@B-Fe/Cu-NPs) and filled to the fixed-bed column. Scanning electron microscopy (SEM), zeta potential, and atomic forces spectroscopy (AFM) techniques were used to characterize the obtained particles (NPs). The effect of flow rate and DB15 concent