The - mixing ratios of -transitions from levels in populated in the reactions are calculated in present work using - ratio, constant statisticalTensor and least squares fitting methods The results obtained are in general, in good agreement or consistent, within the associated uncertainties, with these reported in Ref.[9],the discrepancies that occurs are due to inaccuracy existing in the experimental data The results obtained in the present work confirm the –method for mixed transitions better than that for pure transition because this method depends only on the experimental data where the second method depends on the pure or those considered to be pure -transitions, the same results occur in – method
Thin films of (CuO)x(ZnO)1-x composite were prepared by pulsed laser deposition technique and x ratio of 0≤ x ≤ 0.8 on clean corning glass substrate at room temperatures (RT) and annealed at 373 and 473K. The X-ray diffraction (XRD) analysis indicated that all prepared films have polycrystalline nature and the phase change from ZnO hexagonal wurtzite to CuO monoclinic structure with increasing x ratio. The deposited films were optically characterized by UV-VIS spectroscopy. The optical measurements showed that (CuO)x(ZnO)1-x films have direct energy gap. The energy band gaps of prepared thin films
Liquid – liquid interface reaction is the method for
preparation nanoparticles (NP'S) which depend on the super
saturation of ions that provide by using the system that consist from
toluene and water, the first one is above the second to obtain
nanoparticles (NP's) CdS at the interface separated between these
two immiscible liquid. The structure properties were characterized by
XRD-diffraction and transmission electron microscopy.
The crystalline size estimate from X-ray diffraction pattern
using Scherer equation to be about 7nm,and by TEM analysis give us
that ananosize is about 5 nm which give a strong comparable with
Bohr radius. Photoluminescence analysis give two emission peak,
the first one around
Experimental measurements were done for characterizing current-voltage and power-voltage of two types of photovoltaic (PV) solar modules; monocrystalline silicon (mc-Si) and copper indium gallium di-selenide (CIGS). The conversion efficiency depends on many factors, such as irradiation and temperature. The assembling measures as a rule cause contrast in electrical boundaries, even in cells of a similar kind. Additionally, if the misfortunes because of cell associations in a module are considered, it is hard to track down two indistinguishable photovoltaic modules. This way, just the I-V, and P-V bends' trial estimation permit knowing the electrical boundaries of a photovoltaic gadget with accuracy. This measure
... Show MoreThe purpose of this research is to find the estimator of the average proportion of defectives based on attribute samples. That have been curtailed either with rejection of a lot finding the kth defective or with acceptance on finding the kth non defective.
The MLE (Maximum likelihood estimator) is derived. And also the ASN in Single Curtailed Sampling has been derived and we obtain a simplified Formula All the Notations needed are explained.
The semiconductor ZnO is one of II – VI compound group, it is prepare as thin films by using chemical spray pyrolysis technique; the films are deposited onto glass substrate at 450 °C by using aqueous zinc chloride as a spray solution of molar concentration 0.1 M/L. Sample of the prepared film is irradiating by Gamma ray using CS 137, other sample is annealed at 550°C. The structure of the irradiated and annealed films are analyzed with X-ray diffraction, the results show that the films are polycrystalline in nature with preferred (002) orientation. The general morphology of ZnO films are imaged by using the Atomic Force Microscope (AFM), it constructed from nanostructure with dimensions in order of 77 nm.
The optical properties o
Tetradentate complexes type [M (HL) 2] were prepared from the reaction of 2-hydroxy -1, 2-diphynel-ethanone oxime [H2L] and KOH with ( Mn II, Fe II, Co II, Ni II , Cu II and Hg II ), in methanol with (2:1) metal: ligand ratio. The general formula for Cu II and Mn II complexes are [M (HL) 2 Cl.H2O] K, for Co II [Co (HL) 2. H2O] and [M (HL) 2] for the rest of complexes. All compounds were characterised by spectroscopic methods, I.R, U.V-Vis, H.P.L.C, atomic absorption and conductivity measurements chloride content. From the data of these measurements, the proposed molecular structures for Fe II and Hg II complexes are tetrahedrals, while Mn II and Cu II complexes are octahedrals, Ni II complex adopting square planar structure and the complex
... Show MoreThe permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.
This study examines the removal of ciprofloxacin in an aqueous solution using green tea silver nanoparticles (Ag-NPs). The synthesized Ag-NPs have been classified by the different techniques of SEM, AFM, BET, FTIR, and Zeta potential. Spherical nanoparticles with average sizes of 32 nm and a surface area of 1.2387m2/g are found to be silver nanoparticles. The results showed that the ciprofloxacin removal efficiency depends on the initial pH (2.5-10), CIP (2-15 mg/L), temperature (20-50°C), time (0-180 min), and Ag-NPs dosage (0.1-1g/L). Batch experiments revealed that the removal rate with ratio (1:1) (w/w) were 52%, and 79.8% of the 10 mg/L of CIP at 60, and 180 minutes, respectively with optimal pH=4. Kinetic models for adsorpti
... Show More
Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show More