The CdSe pure films and doping with Cu (0.5, 1.5, 2.5, 4.0wt%) of thickness 0.9μm have been prepared by thermal evaporation technique on glass substrate. Annealing for all the prepared films have been achieved at 523K in vacuum to get good properties of the films. The effect of Cu concentration on some of the electrical properties such as D.C conductivity and Hall effect has been studied.
It has been found that the increase in Cu concentration caused increase in d.c conductivity for pure CdSe 3.75×10-4(Ω.cm)-1 at room temperatures to maximum value of 0.769(Ω.cm)-1 for 4wt%Cu.All films have shown two activation energies, where these value decreases with increasing doping ratio. The maximum value of activation energy was (0.319)eV f

Films of CdSe have been prepared by evaporation technique with thickness 1µm. Doping with Cu was achieved using annealing under argon atmosphere . The Structure properties of these films are investigated by X-ray diffraction analysis. The effect of Cu doping on the orientation , relative intensity, grain size and the lattice constant has been studied. The pure CdSe films have been found consist of amorphous structure with very small peak at (002) plane. The films were polycrystalline for doped CdSe with (1&2wt%) Cu contents and with lattice constant (a=3.741,c=7.096)A°, and it has better crystallinty as the Cu contents increased to (3&5wt%) Cu. The reflections from [(002), (102). (110), (112), and (201)]planes are more prominen

ABSTRACT

            A laboratory experiment was carried out during winter season of 2021 in the Seed Technology Laboratory- College of Agricultural Engineering Sciences/ University of Baghdad, to find out the allopathic effects of aerobic and terrestrial aqueous extracts of Artemisia vulgaris L. on the seed germination and seedling growth of linseed. A factorial experiment according to a completely randomized design (CRD)at three replicates was used; the first factor in clouded type of aqueous extract for two plant parts which were aerobic (stems and leaves) and terrestrial (root and rhizomes), while the second factor included five concentrations

In this study, iron oxide nanoparticles (α-Fe₂O₃ NPs) were prepared using a readily available chili pepper plant extract from local markets. This study aims to evaluate the magnetic properties of α-Fe₂O₃ prepared in green chemistry from Capsicum plant extract. After several simple preparatory steps, such as washing and cutting, they were treated with an inorganic complex (potassium hexacyanoferrate) (K3[Fe(CN)₆]). In the first analytical step, the in vitro detection of the plant extract solution after reaction with the potassium hexacyanoferrate (III) complex revealed characteristic adsorption bands of the cyanide group, which disappeared upon complexation. The iron oxide NPs were characterized using various methods, including X

In this work, two graphene oxide (GO) samples were prepared using the Hummers method with graphite (g) and KMnO4 (g) ratios of 1:3 (GO3) and 1:6 (GO6). The effect of oxidation degree on the structural, electrical, and dielectric properties of the GO samples was investigated. The structures of the GO samples were studied using various techniques, including X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDXS). XRD analysis revealed an increase in the interlayer spacing and a decrease in the number of layers of the samples with increasing oxidant content. The two GO samples have giant permittivity values of ~105 in the low-frequency

In this research we assumed that the number of emissions by time (𝑡) of radiation particles is distributed poisson distribution with parameter (𝑡), where  < 0 is the intensity of radiation. We conclude that the time of the first emission is distributed exponentially with parameter 𝜃, while the time of the k-th emission (𝑘 = 2,3,4, … . . ) is gamma distributed with parameters (𝑘, 𝜃), we used a real data to show that the Bayes estimator 𝜃 ∗ for 𝜃 is more efficient than 𝜃̂, the maximum likelihood estimator for 𝜃 by using the derived variances of both estimators as a statistical indicator for efficiency