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Bilayer MSe<sub>2</sub> (M = Zr, Hf, Mo, W) performance as a hopeful thermoelectric materials
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Abstract<p>Significant advancements in nanoscale material efficiency optimization have made it feasible to substantially adjust the thermoelectric transport characteristics of materials. Motivated by the prediction and enhanced understanding of the behavior of two-dimensional (2D) bilayers (BL) of zirconium diselenide (ZrSe<sub>2</sub>), hafnium diselenide (HfSe<sub>2</sub>), molybdenum diselenide (MoSe<sub>2</sub>), and tungsten diselenide (WSe<sub>2</sub>), we investigated the thermoelectric transport properties using information generated from experimental measurements to provide inputs to work with the functions of these materials and to determine the critical factor in the trade-off between thermoelectric materials. Based on the Boltzmann transport equation (BTE) and Barden-Shockley deformation potential (DP) theory, we carried out a series of investigative calculations related to the thermoelectric properties and characterization of these materials. The calculated dimensionless figure of merit (ZT) values of 2DBL-MSe<sub>2</sub> (M = Zr, Hf, Mo, W) at room temperature were 3.007, 3.611, 1.287, and 1.353, respectively, with convenient electronic densities. In addition, the power factor is not critical in the trade-off between thermoelectric materials but it can indicate a good thermoelectric performance. Thus, the overall thermal conductivity and power factor must be considered to determine the preference of thermoelectric materials.</p>
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Publication Date
Mon Aug 19 2024
Journal Name
Chalcogenide Letters
Influence of copper on zinc oxide films and solar cell performance
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Copper doped Zinc oxide and (n-ZnO / p-Si and n-ZnO: Cu / p-Si) thin films thru thickness (400±20) nm were deposited by thermal evaporation technique onto two substrates. The influence of different Cu percentages (1%,3% and 5%) on ZnO thin film besides hetero junction (ZnO / Si) characteristics were investigated, with X-ray diffractions examination supports ZnO films were poly crystal then hexagonal structural per crystallite size increase from (22.34 to 28.09) nm with increasing Cu ratio. The optical properties display exceptional optically absorptive for 5% Cu dopant with reduced for optically gaps since 3.1 toward 2.7 eV. Hall Effect measurements presented with all films prepared pure and doped have n-types conductive, with a ma

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Publication Date
Wed Jan 01 2025
Journal Name
Lecture Notes In Networks And Systems
Comparison Between the Performance of Human Translators and AI-Supported Applications
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This study aims to conduct an exhaustive comparison between the performance of human translators and artificial intelligence-powered machine translation systems, specifically examining the top three systems: Spider-AI, Metacate, and DeepL. A variety of texts from distinct categories were evaluated to gain a profound understanding of the qualitative differences, as well as the strengths and weaknesses, between human and machine translations. The results demonstrated that human translation significantly outperforms machine translation, with larger gaps in literary texts and texts characterized by high linguistic complexity. However, the performance of machine translation systems, particularly DeepL, has improved and in some contexts

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Publication Date
Thu Dec 01 2016
Journal Name
Optik
Effect of hydrothermal growth time on ZnO nanorod arrays photoelectrode performance
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Publication Date
Tue Jan 01 2019
Journal Name
Indian Journal Of Public Health Research &amp; Development
Hemodialysis Nurses’ Practices toward Hand Hygiene Performance at Baghdad Teaching Hospitals
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Publication Date
Wed Jun 01 2022
Journal Name
Journal Of Engineering Science And Technology
THERMAL PERFORMANCE OF MULTIPLE- PASS SOLAR AIR HEATER WITH PIN FINS
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Publication Date
Sun Jun 01 2025
Journal Name
Results In Engineering
Structural Performance of Rail Connections: Experimental Testing and Finite Element Modeling
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Publication Date
Fri Oct 03 2025
Journal Name
Mesopotamian Journal Of Computer Science
Enhanced TEA Algorithm Performance using Affine Transformation and Chaotic Arnold Map
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In digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th

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Publication Date
Sun Sep 01 2024
Journal Name
Gazi University Journal Of Science
Effect of Natural Bitumen on the Performance of Hot Asphalt Mixture‏
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For over a century, the global consumption of asphalt binder in asphalt mixture production has been substantial. In the Heet region (west of Iraq), two distinct forms of natural asphalt (NA) deposits exist: rock asphalt and sulfur spring asphalt. This study focused on using NA sourced from sulfur springs. The aim was to investigate the potential of incorporating NA into local asphalt mixtures. To achieve this, NA was heated to 163°C for varying durations. After heat treatment, laboratory tests were conducted on NA. The findings suggest that by heating NA for 20 hours, it conforms to Iraqi specifications in terms of physical properties. Furthermore, compared to conventional petroleum asphalt, treated NA showed greater

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Publication Date
Sun May 01 2022
Journal Name
Journal Of Engineering
Performance Analysis of different Machine Learning Models for Intrusion Detection Systems
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In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

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Publication Date
Tue Oct 01 2019
Journal Name
Journal Of Engineering
Characterization Performance of Monocrystalline Silicon Photovoltaic Module Using Experimentally Measured Data
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Solar photovoltaic (PV) system has emerged as one of the most promising technology to generate clean energy. In this work, the performance of monocrystalline silicon photovoltaic module is studied through observing the effect of necessary parameters: solar irradiation and ambient temperature. The single diode model with series resistors is selected to find the characterization of current-voltage (I-V) and power-voltage (P-V) curves by determining the values of five parameters ( ). This model shows a high accuracy in modeling the solar PV module under various weather conditions. The modeling is simulated via using MATLAB/Simulink software. The performance of the selected solar PV module is tested experimentally for differ

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