The increasing demand for energy has encouraged the development of renewable resources and environmentally benign fuel such as biodiesel. In this study, ethyl fatty esters (EFEs), a major component of biodiesel fuel, were synthesized from soybean oil using sodium ethoxide as a catalyst. By-products were glycerol and difatty acyl urea (DFAU), which has biological characteristics, as antibiotics and antifungal medications. Both EFEs and DFAU have been characterized using Fourier transform infrared (FTIR) spectroscopy, and 1H nuclear magnetic resonance (NMR) technique. The optimum conditions were studied as a function of reaction time, reactant molar ratios, catalyst percentage and the effect of organic solvents. The conversion ratio of soybean oil into pure EFEs was 76% after 10 h of reaction. The highest conversion yield of EFEs is obtained when the urea/soybean oil ratio was from 6.2 mmol to 1 mmol, while the highest production of DFAU is obtained when the ethoxide (as a catalyst)/soybean oil ratio is from 6.4 mmol to 1 mmol in hexane as the reaction medium.
In this work, new compounds containing 1,2,4-oxadiazole units were synthesized, characterized, and their crystalline liquid qualities were also studied. The synthesis of compound [I ] starts with the reaction 4-hydroxybenzonitrile with phthaloyl chloride to yield nitrile compound. Then the later compound reacted with hydroxyl amine hydrochloride to obtain compound [II]. Next, 1,2,4-Oxadiazole compounds [III]a-c and [IV] were obtained by the reaction between compound [II] with various acid chlorides(acetyl chloride, chloroacetyl chloride, benzoyl chloride) and phthaloyl chloride in pyridine, respectively. FTIR, 1HNMR and mass spectroscopy were used to analyze the synthesized compounds Spectroscopic investigation verified the synthetic
... Show MoreThis research, involved synthesis of some new 1,2,3-triazoline and 1,2,3,4- tetrazole derivatives from antharanilic acid as starting material .The first step includes formation of 2-Mercapto-3-phenyl-4(3H)Quinazolinone (0) through reacted of anthranilic acid with phenylisothiocyanate in ethanol, then compound (0) reaction with chloro acetyl chloride in dimethyl foramamide (DMF) to prepare intermediate S-(α-chloroaceto-2-yl)-3-phenylquinazolin-4(3H)-one (1); compound (1) reacted with sodium azide to yield S-(α-azidoaceto-2-yl)-3-phenylquinazolin-4(3H)-one (2), while Schiff bases (3-10) were prepared from condensation of substituted primary aromatic amines with different aromatic aldehydes in absolute ethanol as a solvent. Compound (2)
... Show MoreIn this paper, two types of iron oxide nanomaterial (Fe3O4) and nanocomposite (T-Fe3O4) were created from the bio-waste mass of tangerine peel. These two materials were utilized for adsorption tests to remove cefixime (CFX) from an aqueous solution. Before the adsorption application, both adsorbents have been characterized by various characterizations such as XRD, FTIR, VSM, TEM, and FESEM. The mesoporous nano-crystalline structure of Fe3O4 and T-Fe3O4 nanocomposite with less than 100-nm diameter is confirmed. The adsorption of the obtained adsorbents was evaluated for CFX removal by adjusting several operation parameters to optimize the removal. The optimal conditions for CFX removal were found to be an initial concentration of 40 and 50 m
... Show MoreThe investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti
... Show MoreObjective: Synthesis, Characterization of formazan derivatives and studies the antioxidant activity of prepared compounds and molecular docking. Methods: In this study, formazan compounds (III–XIV) were produced by combining Schiff base compounds (I), (II) with diazonium salts resulting from reactions of different aromatic amines with sodium nitrate in the presence of Con.HCl at 0–5°C. When isonicotinic acid hydrazide reacts with (N,N-dimethylbenzaldehyde, 4-hydroxy-3-methoxybenzaldehyde) in the presence glacial acetic acid as a solvent Schiff base compounds are created. Results: The prepared compounds were identified by FT-IR, 1H NMR, 13C NMR, then the antioxidant activity of the derivatives and molecular docking were studied. D
... Show MoreTwo Schiff base ligands L1 and L2 have been obtained by condensation of salicylaldehyde respectively with leucylalanine and glycylglycine then their complexes with Zn(II)were prepared and characterized by elemental analyses , conductivity measurement , IR and UV-Vis .The molar conductance measurement indicated that the Zn(II) complexes are 1:1 non-electrolytes. The IR data demonstrated that the tetradentate binding of the ligands L1 and L2 . The in vitro biological screening effect of the investigated compounds have been tested against the bacterial species Staphlococcus aureus, Escherichia coil , Klebsiella pneumaniae, Proteus vulgaris and Pseudomonas aeruginosa by the disc diffusion method . A comparative study of inhibition values of
... Show MoreThe 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
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