Several schottky diodes were fabricated from polyaniline/ Carbon nanotube (single and multiwalled) composites. These composites were synthesized with different concentration and two carbon nanotubes types, Single and Multi-Walled Carbon Nanotubes (SWCNT & MWCNT). Aluminum and silver paste were chosen as schottky and ohmic contact respectively. physical and electrical were used to studied these composite by using Atomic Force Microscopy (AFM) and electrical measurements. The Root Mean Square RMS surface roughness of the composite samples was found to be around 4nm. The currentvoltage characteristic were measurements for all samples in the bias range ±15V at room temperature. The results shows the increasing in carbon nanotubes concentration

Hand-lay up method was used to prepare the samples made of epoxy (EP) as a matrix reinforced with chopped carbon fibers (CCF). The fatigue behavior of epoxy resin /chopped carbon fiber composites was studied with different weight percentage of chopped carbon fibers (2.5%,5%,7.5%,10%,12.5%). The fatigue test was carried out under alternate bending method, which was made by applying sinusoidal wave with constant displacement (15mm), stress ratio R=-1,and loading frequency 10Hz, which is believed to give a negligible temperature rise during the test. The results of the maximum stress, fatigue strength, fatigue limit and fatigue life of the tested composites are calculated from stress(S)-number of cycles(N) (S-N) curves.
It was shown that

There are no single materials which can withstand all the extreme operating conditions in modern technology.  Protection of the metals from hostile environments has therefore become a technical and economic necessity.  

In this work, for enhancing their wear-resistance, boride layers were deposited on the surface of low carbon steel by a pack cementation method at 850 °C for (2, 4, and 6) h using vacuum furnace. The boronizing process was achieved using different concentration of boron source (20, 25, and 30) % wt. into coating mixture to optimize the best conditions which ensure the higher properties with lower time. The coating was characteristic by X ray diffraction (XRD), and it is confirmed t

The thermal method was used to produce silicoaluminophosphate (SAPO-11) with different amounts of carbon nanotubes (CNT). XRD, nitrogen adsorption-desorption, SEM, AFM, and FTIR were used to characterize the prepared catalyst. It was discovered that adding CNT increased the crystallinity of the synthesize SAPO-11 at all the temperatures which studied, wile the maximum surface area was 179.54 m2/g obtained at 190°C with 7.5 percent of CNT with a pore volume of 0.317 cm3/g ,and with nano-particles with average particle diameter of 24.8 nm, while the final molar composition of the prepared SAPO-11 was (Al2O3:0.93P2O5:0.414SiO2).

Overlapped have been prepared from epoxy resin material added to carbon Nanotube and percentages weight (0.1, 0.05, 0.01) % Studied the mechanical properties of the composite (bending, tensile an d hardness) has been found that the Flexural and tensile modulus of the composites were higher than the pure epoxy resin this may be due to the high mechanical strength of carbon nano tube (CNT). The hardness of the epoxy carbon Nanotube composites increased and the reason is due to increased overlap and stacking between the additives and material basis, which reduces the movement of polymer molecules leading to increased resistance to scratching material and cutting, will become more resistance to plastic deformation.

       Currently, there is no established of e-waste treatment in Thi- Qar province, while their creating is increasing every year. It has been well- known that e-waste is a source of environmental degrading and their placement in landfills increases the irreversible climate change. A research model has been developed to link three components: coercive pressure, normative influence, and mimicry, then study their effect on e-waste adopt and continuance intentions. The model was validated using data collected from a field survey of 92 managers of small enterprises in Thi-Qar province.  A questionnaire was developed to collect data. It contains five major variables, exemplify by fourteen items. Als

This research aims to find out the phonemic, dialect and semantic ailments that were interrogated by the book of the parsing of thirty surahs of the Noble Qur’an by Ibn Khalawiyeh, by examining some phonemic phenomena of Qur’anic expressions and their explanatory relationships that resulted in their occurrence, such as substitution and compression, as the sounds in a word are affected by one another, especially during performance and composition. This influence and influence is the tendency of the human being to the law of ease, facilitation, and the reduction of muscular effort in speech often, all for the purpose of obtaining phonemic harmony for the compound letters; To facilitate pronunciation; And get rid of the muscular effort

Understanding the compatibility between spider silk and conducting materials is essential to advance the use of spider silk in electronic applications. Spider silk is tough, but becomes soft when exposed to water. Here we report a strong affinity of amine-functionalised multi-walled carbon nanotubes for spider silk, with coating assisted by a water and mechanical shear method. The nanotubes adhere uniformly and bond to the silk fibre surface to produce tough, custom-shaped, flexible and electrically conducting fibres after drying and contraction. The conductivity of coated silk fibres is reversibly sensitive to strain and humidity, leading to proof-of-concept sensor and actuator demonstrations.

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet