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

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet

Steel fiber aluminum matrix composites were prepared by atomization technique. Different air atomization conditions were considered; which were atomization pressure and distance between sample and nozzle. Tensile stress properties were studied. XRF and XRD techniques were used to study the primary compositions and the structure of the raw materials and the atomized products. The tensile results showed that the best reported tensile strength observed for an atomization pressure equal to 4 mbar and sample to nozzle distance equal to 12 cm. Young modulus results showed that the best result occurred with an air atomization pressure equal to 8 mbar and sample to nozzle distance equal to 16cm

    Seven Zn-dithiocarbamate complexes were suggested as corrosion inhibitors. Density functional theory (DFT) was used to predict the ability of inhibition. Room temperature conditions were applied to suggest the optimization of complexes, physical properties, and corrosion parameters. In addition, the HOMO, LUMO, dipole moment, energy gap, and other parameters were used to compare the inhibitors efficiency. Gaussian 09 software with LanL2DZ basis set was used. Total electron density (TED) and electrostatic surface potential (ESP) were utilized to show the sites of adsorption according to electron density.

Background: Denture cleansing was an important step that could prevent the spread of infection and improve a patient's health, the durability of the dentures, and the overall quality of life; therefore, it was necessary to choose a suitable cleanser that, in addition to being effective, did not have an unfavorable effect on the qualities of the denture base resin itself when used for an extended period. For this purpose, this study aimed to evaluate the effect of tea tree oil (TTO) on Candida albicans adhesion and the surface roughness property of poly(methyl methacrylate) denture material after immersion in TTO. Methods: A total of 55 heat-cured acrylic resin specimens were used for C. albicans adherence and surface roughness tests. The