Zinc oxide (ZnO) nanoparticles were synthesized using a modified hydrothermal approach at different reaction temperatures and growth times. Moreover, a thorough morphological, structural and optical investigation was demonstrated using scanning electron microscopy (SEM), x-ray diffraction (XRD), ultra-violate visible light spectroscopy (UV-Vis.), and photoluminescence (PL) techniques. Notably, SEM analysis revealed the occurrence of nanorods-shaped surface morphology with a wide range of length and diameter. Meanwhile, a hexagonal crystal structure of the ZnO nanoparticles was perceived using XRD analysis and crystallite size ranging from 14.7 to 23.8 nm at 7 and 8 ℎ𝑟𝑠., respectively. The prepared ZnO samples showed good abso

CdS and CdS:Sn thin films were successfully deposited on glass
substrates by spray pyrolysis method. The films were grown at
substrate temperatures 300 C°. The effects of Sn concentration on the
structural and optical properties were studied.
The XRD profiles showed that the films are polycrystalline with
hexagonal structure grown preferentially along the (002) axis. The
optical studies exhibit direct allowed transition. Energy band gap
vary from 3.2 to 2.7 eV.

ZnO nanostructures were synthesized by hydrothermal method at different temperatures and growth times. The effect of increasing the temperature on structural and optical properties of ZnO were analyzed and discussed. The prepared ZnO nanostructures were characterized by X-ray diffraction (XRD), UV–Vis. absorption spectroscopy (UV–Vis.), Photoluminescence (PL), and scanning electron microscopy (SEM). In this work, hexagonal crystal structure prepared ZnO nanostructures was observed using X-ray diffraction (XRD) and the average crystallite size equal 14.7 and 23.8 nm for samples synthesized at growth time 7 and 8 hours respectively. A nanotubes-shaped surface morphology was found using scanning electron microscopy (SEM). The optic

Thin filis have been prepared from the tin disulphide (SnS2 ), the pure and the doped with copper (SnS2:Cu) with a percentages (1,2,3,4)% by using ahemical spray pyrolysis techniqee on substrate of glass heated up to(603K)and sith thicknesses (0.7±0.02)?m ,after that the films were treated thermally with a low pressure (10-3mb) and at a temperature of (473K) for one hour. The influence of both doping with copper and the thermal treatment on some of the physical characteristics of the prepared films(structural and optical) was studied. The X-ray analysis showed that the prepared films were polycrystalline Hexagonal type. The optical study that included the absorptance and transmitance spectra in the weavelength range (300-900)nm

Undoped and Iodine (I)–doped chrome oxide (Cr2O3)thin films have been prepared by chemical spray pyrolysis technique at substrate temperatures(773K) on glass substrate. Absorbance and transmittance spectra have been recorded as a function of wavelength in the range (340-800 nm) in order to study the optical properties such as reflectance, Energy gap of allowed direct transition, extinction coefficient refractive index, and dielectric constant in real and imagery parts all as a function of wavelength. It was found that all the investigated parameters affect by the doping ratios.

A low-cost reverse flow plasma system powered by argon gas pumping was built using homemade materials in this paper. The length of the resulting arc change was directly proportional to the flow rate, while using the thermal camera to examine the thermal intensity distribution and demonstrating that it is concentrated in the centre, away from the walls at various flow rates, the resulting arc's spectra were also measured. The results show that as the gas flow rate increased, so did the ambient temperature. The results show that the medium containing the arc has a maximum temperature of 34.1 ˚C at a flow rate of 14 L/min and a minimum temperature of 22.6 ˚C at a flow rate of 6 L/min.

Abstract 

This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control per