The combination of carbon nanotubes (CNT) and conducting polymers offers an attractive route for the production of novel compounds that can be used in a variety of applications such as sensors, actuators, and molecular scale electronic devices. In this work, functionalized multiwall carbon nanotubes (f-MWCNTs) were added in different load ratios (3 wt%, 5 wt% and 10 wt%) to thiophen (PTh) polymer to procedure PTh/CNTs nanocomposite and deposited on porous silicon substrate by electropolarization. Photoconductive detectors were fabricated using PTh/f-MWCNTs matrix to work in the near region and middle IR regions. These detectors were illuminated by semiconductor laser diode wavelength of 808(nm) and Nd-YAG laser of wavelength 1064 (nm) to study the I-V characteristics and figures of merit. FTIR spectra assignments verify that the thiophene groups were successfully introduced into the carbon nanotubes. SEM images reflect that the electro polymerization process gives well coating for the CNTs by PTh. The conductivity of PTh as a function of temperature increased about 30 times due to addition of f-MWCNTs. Figures of merit reflect a good IR radiation sensitivity and photo response. The specific detectivity was in order of 108 (cm.Hz1/2/W) for both IR regions. The rise and fall times of the output signal are about of 192 (μs) and 121 (μs) respectively for load ratio 5wt% of CNT, which consider good values for these types of detectors.
The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est
... Show MoreZinc Oxide (ZnO) is considered as one of the best materials already used as a window layer in solar cells due to its antireflective capability. The ZnO/MgF2 bilayer thin film is more efficient as antireflective coating. In this work, ZnO and ZnO/MgF2 thin films were deposited on glass substrate using pulsed laser deposition and thermal evaporation deposition methods. The optical measurements indicated that ZnO thin layer has an energy gap of (3.02 eV) while ZnO/MgF2 bilayer gives rise to an increase in the energy gap. ZnO/MgF2 bilayer shows a high energy gap (3.77 eV) with low reflectance (1.1-10 %) and refractive index (1.9) leading to high transmittance, this bilayer could be a good candidate optical material to improve the performance
... Show MoreKE Sharquie, AA Noaimi, AG Al-Ghazzi, 2010 - Cited by 2
This study introduced the effect of using magnetic abrasive finishing method (MAF) for finishing flat surfaces. The results of experiment allow considering the MAF method as a perspective for finishing flat surfaces, forming optimum physical mechanical properties of surfaces layer, removing the defective layers and decreasing the height of micro irregularities. Study the characteristics which permit judgment parameters of surface quality after MAF method then comparative with grinding
Sustainable development has recently gained significant attention in the field of water Quality (WQ), which is critical for a healthy lifestyle. Our work suggests an intelligent hybrid model for assessing water quality using a water portability dataset, which was used to evaluate the water quality. The dataset contains physical and chemical features such as pH, Organic Carbon, sulfate, etc. Based on the combination of a Decision Tree Algorithm (DTA) and fuzzy logic Approaches, the findings revealed that sulfate was the most important factor in the model, with an accuracy of 1.00, followed by pH with an accuracy of 0.9667, and solids and chloramines with an accuracy of 0.95. The other parameters achieved a similar accuracy of 0.9167, showing
... Show MoreWhenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas
... Show MoreAs one type of resistance furnace, the electrical tube furnace (ETF) typically experiences input noise, measurement noise, system uncertainties, unmodeled dynamics and external disturbances, which significantly degrade its temperature control performance. To provide precise, and robust temperature tracking performance for the ETF, a robust composite control (RCC) method is proposed in this paper. The overall RCC method consists of four elements: First, the mathematical model of the ETF system is deduced, then a state feedback control (SFC) is constructed. Third, a novel disturbance observer (DO) is designed to estimate the lumped disturbance with one observer parameter. Moreover, the stability of the closed loop system including controller
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