This research presents a study in ultra-desulfurization of diesel fuel produced from conventional hydro desulfurization process, using oxidation and solvent extraction techniques. Dibenzothiophene (DBT) was the organosulfur compound that had been detected in sulfur removal. The oxidation process used hydrogen peroxide as an oxidant and acetic acid as homogeneous catalyst . The solvent extraction process used acetonitrile (ACN) and N-methyl – 2 - pyrrolidone (NMP) as  extractants . Also the effect of five parameters (stirring speed :150 , 250 , 350 , and 450) rpm, temperature (30 , 40 , 45 , and 50) ^oC, oxidant/simulated diesel fuel ratio (0.5 , 0.75 , 1 , and 1.5) , catalyst/oxidant ratio(0.125,0.25,0.5

The present research was conducted to reduce the sulfur content of Iraqi heavy naphtha by adsorption using different metals oxides over Y-Zeolite. The Y-Zeolite was synthesized by a sol-gel technique. The average size of zeolite was 92.39 nm, surface area 558 m²/g, and pore volume 0.231 cm³/g. The metals of nickel, zinc, and copper were dispersed by an impregnation method to prepare Ni/HY, Zn/HY, Cu/HY, and Ni + Zn /HY catalysts for desulfurization. The adsorptive desulfurization was carried out in a batch mode at different operating conditions such as mixing time (10,15,30,60, and 600 min) and catalyst dosage (0.2,0.4,0.6,0.8,1, and 1.2 g). The most of the sulfur compounds were removed at 10 min for all catalyst ty

         Beryllium Zinc Oxide (Be_xZn_1-xO) ternary nano thin films were deposited using the pulsed laser deposition (PLD) technique under a vacuum condition of 10^-3 torr at room temperature on glass substrates with different films thicknesses, (300, 600 and 900 nm). UV-Vis spectra study found the optical band gap for Be_0.2Zn_0.8O to be  (3.42, 3.51 and 3.65 eV) for the (300, 600 and 900nm) film thicknesses, respectively which is larger than the value of zinc oxide ZnO (3.36eV) and smaller than that of beryllium oxide BeO (10.6eV). While the X-ray diffraction (XRD) pattern analysis of ZnO, BeO and Be _0.2 Zn _0.8 O powder and nano-thin films indicated a hexa

The dielectric constant of most polymers is very low; the addition of TiO2 particles into the polymers provides an attractive and promising way to reach a high dielectric constant. Polymer-based materials with a high dielectric constant show great potential for energy storage applications. Four samples were prepared, one of them was polyurethane (PU) and the other were PU with different weight percent (wt %) of TiO2 (0.1, 0.2, 0.3) powder AFM test was used to distinguish the nanoparticles. The result shows that the most shape of these nanoparticles are spherical and the roughness average is 0.798 nm. The dielectric properties were measured for all samples before and after the exposure to the UV radiation. The result illustrates that the

Polyaniline films were successfully synthesized in this study using an oxidative polymerization method at temperatures ranging from 0 to 4 ° C. Polyaniline films were deposited using a single step of chemical oxidative polymerization rather than electrochemical polymerization. The polyaniline was examined using FTIR, XRD, SEM, AFM, and Four Point Probe. This result demonstrates that polyaniline synthesized using this method has a uniform morphology, small size (17 to 40) nm, high crystallinity, and high conductivity (9.42 s/cm).

In this research, titanium dioxide nanoparticles (TiO₂ NPs) were prepared through the sol-gel process at an acidic medium (pH3).TiO₂ nanoparticles were prepared from titanium trichloride (TiCl₃) as a precursor with Ammonium hydroxide (NH₄OH) with 1:3 ratio at 50 °C. The resulting gel was dried at 70 °C to obtain the Nanocrystalline powder. The powder from the drying process was treated thermally at temperatures 500 °C and 700 °C. The crystalline structure, surface morphology, and particle size were studied by using X-ray diffraction (XRD), Atomic Force Microscopy (AFM), and Scanning Electron Microscope (SEM). The results showed (anatase) phase of titanium dioxide with the average grain size

Many condensed polymers [A1-A7] were prepared via reaction of (Ethylenediaminetetraacetic acid = EDTA), with different prepared imide-diamines by modification [ modification of amino acids and antibiotics (B1- B7)] Imide-diamines were prepared by chlorination of L-amino acids such as [ L-Histidine, L-Alanine, L-Valine, L-Glycine and L-Aspargine ] or selected antibiotics such as [Cephallixine monohydrate and Amoxilline ] with thionyl chloride at 0°C, then reacted with ammonia to obtain imidediamines [B1-B7] . The physical properties of all prepared condensed polymers [A1-A7], new prepared diamines [B1-B7] were studied and characterized by FT -IR spectroscope to certify the structural formulas. The thermal analysis (TGA, DTA) were studied, a

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve