In this work, we study the effect of doping Sn on the structural and optical properties of pure cadmium oxide films at different concentrations of Tin (Sn) (X=0.1,0.3 and 0.5) .The films prepared by using the laser-induced plasma at wavelength of laser 1064 nm and duration 9 ns under pressure reached to 2.5×10^-2 mbar. The results of X-ray diffraction tests showed that the all prepared films  are polycrystalline. As for the topography of the films surface, it was measured using AFM , where the results showed that the grain size increases with an increase in the percentage of doping  in addition to an increase in the average roughness. The optical properties of all films have also been studied through the absorbance s

In this research, the X-ray diffraction pattern was used, which was obtained experimentally after preparation of barium oxide powder. A program was used to analyze the X-ray diffraction lines of barium oxide nanoparticles, and then the particle size was calculated by using the Williamson-Hall method, where it was found that the value of the particle size is 25.356 nm. Also, the dislocation density was calculated, which is equal to1.555 x1015 (lines/nm2), and the value of the unit cell number was also calculated, as it is equal to 23831.

Alloys were performed from In2O3 doped SnO2 with different doping ratio by quenching from the melt technique. Pulsed Laser Deposition PLD was used to deposit thin films of different doping ratio In2O3 : SnO2 ( 0, 1, 3, 5, 7 and 9 % wt.) on glass substrate at ambient temperature under vacuum of 10 -3 bar thickness of ~100nm. The structural type ,grain size and morphology of the prepared alloys compounds and thin films were examined using X-ray diffraction and atomic force microscopy . The results showed that all alloys have polycrystalline structures and the peaks belonged to the preferred plane for crystal growth were identical with the ITO (Indium – Tin –Oxide) standard cards also another peaks were ob

In this study, the stress-strength model R = P(Y < X < Z)  is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used    to estimate the parameters  namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and Shrinkage estimator using three types of shrinkage weight factors. As well as, Monte Carlo simulation are used to compare the estimation methods based on mean squared error criteria.  

We describe the synthesis and characterization of a novel 2D-MnOx material using a combination of HR-TEM, XAS, XRD, and reactivity measurements. The ease with which the 2D material can be made and the conditions under which it can be made implies that water oxidation catalysts previously described as “birnessite-like” (3D) may be better thought of as 2D materials with very limited layer stacking. The distinction between the materials as being “birnessite-like” and “2D” is important because it impacts on our understanding of the function of these materials in the environment and as catalysts. The 2D-MnOx material is noted to be a substantially stronger chemical oxidant than previously noted for other birnessite-like manganese oxi

Inthisstudy,FourierTransformInfraredSpectrophotometry(FTIR),XRay Diffraction(XRD)andlossonignition(LOI),comparativelyemployedtoprovideaquick,relativelyinexpensiveandefficientmethodforidentifyingandquantifyingcalcitecontentofphosphateoresamplestakenfromAkashatsiteinIraq.Acomprehensivespectroscopicstudyofphosphate-calcitesystemwasreportedfirstintheMid-IRspectra(4004000cm-1)usingShimadzuIRAffinity-1,fordifferentcutsofphosphatefieldgradeswithsamplesbeneficiatedusingcalcinationandleachingwithorganicacidatdifferenttemperatures.Thenusingtheresultedspectratocreateacalibrationcurverelatesmaterialconcentrationstotheintensity(peaks)ofFTIRabsorbanceandappliesthiscalibrationtospecifyphosphate-calcitecontentinIraqicalcareousphosphateore.Theirpeakswereass

With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

Been manufacturing detector Altosalih optical pattern contact metal semiconductor through deposition poles of aluminum metal on the chips of crystal cadmium Tleraad (CdTe) with directional [111] and growing with laboratory and annealed at a temperature 80c for 30 minutes and eat Study of some electrical properties nailed and scoutNmadj ??????? copper with non ??????? models to see effect Alichoab well research deals impact Alichoab and frequency detector resistance