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Effect of Sulfur on Characterization of AgInSe<sub>1.8</sub>S<sub>0.2</sub> Thin Film and n-AgInSe<sub>1.8</sub>S<sub>0.2</sub> / p-Si Solar Cell
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Abstract Ternary Silver Indium selenide Sulfur AgInSe1.8S0.2 in pure form and with a 0.2 ratio of Sulfur were fabricated via thermal evaporation under vacuum 3*10-6 torr on glasses substrates with a thickness of (550) nm. These films were investigated to understand their structural, optical, and Hall Characteristics. X-ray diffraction analysis was employed to examine the impact of varying Sulfur ratios on the structural properties. The results revealed that the AgInSe1.8S0.2 thin films in their pure form and with a 0.2 Sulfur ratio, both at room temperature and after annealing at 500 K, exhibited a polycrystalline nature with a tetragonal structure and a predominant orientation along the (112) plane, indicating an enhanced degree of crystallinity. The Atomic Force Microscopy (AFM) was utilized to explore how Sulfur affects roughness of surfaces and sampls Grain Size . Furthermore, optical parameters, such as the optical gap and absorption coefficient, were calculated to assess the influence of Sulfur on the optical properties of the AgInSe1.8S0.2 thin films. The UV/Visible measurements indicated a reduction in the energy band gap to 1.78 eV for AgInSe1.8S0.2 at 500 K, making these films potentially suitable for photovoltaic applications. These thin films exhibited donor characteristics, with an increase in electron concentration observed with higher Sulfur content and annealing temperature

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Publication Date
Sat Aug 31 2019
Journal Name
Iraqi Journal Of Physics
Spectroscopic study the plasma parameters for SnO2 doped ZnO prepared by pulse Nd:YAG laser deposition
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 In this work, plasma parameters such as (electron temperature (Te), electron density (ne), plasma frequency (fp) and Debye length (λD)) were studied using spectral analysis techniques. The spectrum of the plasma was recorded with different energy values, SnO2 and ZnO anesthetized at a different ratio (X = 0.2, 0.4 and 0.6) were recorded. Spectral study of this mixing in the air. The results showed electron density and electron temperature increase in zinc oxide: tin oxide alloy targets. It was located  that  The intensity of the lines increases in different laser peak powers when the laser peak power increases and then decreases when the force continues to increase.

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Publication Date
Sun Oct 21 2018
Journal Name
Iraqi Journal Of Physics
Spectroscopic study the plasma parameters for Pb doped CuO prepared by pulse Nd:YAG laser deposition
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In this work, plasma parameters such as, the electron temperature )Te(, electron density ne, plasma frequency )fp(, Debye length )λD(
and Debye number )ND), have been studied using optical emission spectroscopy technique. The spectrum of plasma with different values of energy, Pb doped CuO at different percentage (X=0.6, 0.7, 0.8) were recorded. The spectroscopic study for these mixing under vacuum with pressure down to P=2.5×10-2 mbar. The results of electron temperature for X=0.6 range (1.072-1.166) eV, for X=0.7 the Te range (1.024-0.855) eV and X=0.8 the Te is (1.033-0.921) eV. Optical properties of CuO:Pb thin films were determined through the optical transmission method using ultraviolet visible spectrophotometer within the ra

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Publication Date
Mon Sep 23 2019
Journal Name
Baghdad Science Journal
A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network
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Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm

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