2,2'-(1-(3,4-bis(carboxydichloromethoxy)-5-oxo-2,5-dihydrofuran-2-yl)ethane-1,2-diyl)bis(oxy)bis(2,2-dichloroacetic acid) a derivative of L-ascorbic acid was prepared by reaction of L-ascorbic acid with trichloroacetic acid (1:4) ratio, in the presence of potassium hydroxide. A series of new metal complexes of this ligand were prepared by a reaction with the chlorides of Cd(II), Co(II), Ni(II), Cu(II) and Zn(II). The new ligand and its complexes were identified by C.H.N., IR, UV-visible spectra, Thermogravimetric analysis (TGA), as well as 1H, 13C-NMR and Mass spectra for ligand L. The complexes were also identified by molar conductance, atomic absorption, magnetic susceptibility and X-ray diffraction for Cu (II) complex. FT-IR spectra of complexes suggest coordination occurs through the (COO- ) as a bidentates bonding mood. Elemental analyses of the all complexes suggest that the metal to ligand ratio is 2:1. The complexes were expected to be octahedral geometry. Antibacterial activity of the L and complexes were tested against different kinds of bacteria.
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The study was carried out to determine the cytotoxic, antioxidant and gastro-protective effect of ethyl-4-[(3,5-di-tert-butyl-2-hydroxybenzylid ene)amino] benzoate (ETHAB) in rats.
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Most companies use social media data for business. Sentiment analysis automatically gathers analyses and summarizes this type of data. Managing unstructured social media data is difficult. Noisy data is a challenge to sentiment analysis. Since over 50% of the sentiment analysis process is data pre-processing, processing big social media data is challenging too. If pre-processing is carried out correctly, data accuracy may improve. Also, sentiment analysis workflow is highly dependent. Because no pre-processing technique works well in all situations or with all data sources, choosing the most important ones is crucial. Prioritization is an excellent technique for choosing the most important ones. As one of many Multi-Criteria Decision Mak
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The research aimed to measure the reality of monetary policy and its role in neutralizing the impact of fluctuations in total domestic oil prices, through the most important monetary policy variable (money supply). An example of this is using a simple technique in the previous example, turning it into a straightforward user interface by (Judd and Kunee). After estimating the impact of the policy with the domestic gross domestic oil prices in Iraq, the effect of fluctuations in the domestic gross domestic oil prices in the simple regression model, while the morale of oil prices was not proven with a negative sign, while the morale of money supply and their impact on the increase of the domestic was proven in the multiple regressio
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