Thermomechanical analysis (TMA) and differential scanning calorimetry (DSC) are used to investigate the effect of molding and annealing of polyester on the behavior of thermal expansion and crystallization since these factors play role in the reprocessing or recycling of the polymer. The dynamic mode of the TMA provides enhanced characterization information about the polyester since it separates the transitions into reversible and irreversible signals, and also reveals the progress of the amorphous regions as the polyester loses strength with the increasing temperature approaching melting. Slow cooling after annealing brings crystallization that may be attributed to molecular chain straightening due to orientation.

  5-(mercapto-1,3,4-thiadiazole-2yl)α,α-(diphenyl)methanol have been synthesized by ring closer of potassium xanthate[which  have been prepared by reaction of benzilic acid hydrazide with carbon disulphide in potassium hydroxide] using conc.sulphuric acid at (0-5)°C scheme(I).       Their characterization was carried out from T.L.C, M.P, FT.IR and 1H-NMR.

The compounds 3-[4̄-(4˭-methoxybenzoyloxy) benzylideneamino]-2-thioxo-imidazolidine-4-one(3)aand 4-(1-(5-oxo- 2-thioxoimidazolidin-1-ylimino)ethyl)phenyl acetate(3)b were prepared from the reaction of aromatic aldehyde or ketone(1)a,bwith thiosemicarbazide to give aryl thiosemicarbazones(2)a,b ,followed by cyclization with ethylchloroacetate in the presence of fused sodium acetate. Treatment the compounds(3)a,bwith 4- hydroxybenzenediazoniumchloride yielded the correspondings4-((4-((4-hydroxyphenyl)diazenyl)-5-oxo-2- thioxoimidazolidin-1-ylimino)methyl)phenyl 4-methoxybenzoate(4)aand4-(1-(4-((4-hydroxyphenyl)diazenyl)-5-oxo-2- thioxoimidazolidin-1-ylimino)ethyl)phenyl acetate(4)b.The new 2-thioxo-imidazolidin-4-one with esters (5-7)a,b sy

MR Younus, 1998

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

Objective: 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

In this work, an organic semiconductor of copper (II) phthalocyanine (CuPc) and Tris(8-hydroxyquinoline) aluminum (III) (Alq3) were entirely dissolved in chloroform with various mixing ratios (1:0,0.75:0.25,0.5:0.5,0.25:0.75,0:1) (w/w) to make thin films. They were deposited on a pre-cleaned glass using a spin-coating process and heat-treated at 473 K in vacuum. X-ray diffraction and a scanning electron microscope were used to investigate the films. XRD analysis reveals that CuPc/Alq3 composites have a polymorphic structure, with the exception of Alq3's amorphous structure, the crystallinity increases after annealing, but decreases when the concentration of Alq3 is increased. The quantity of (CuPc) rod-like structure and (Alq3) grain-lik