In this paper, silicon carbonitried thin films were prepared by the method of photolysis of the silane (SiH4) and ethylene (C2H4) gases, with and without ammonia gas (NH3), which is represented by the ratio between the (PNH3) and (PSiH4 + PC2H4 + PNH3), (which assign by the letter X), X has the values (0, 0.13, 0.33). This method carried out by using TEA-CO2 laser, on glass substrate at (375 oC), deposition rate (0.416-0.833) nm/pulse thin film thickness of (500-1000) nm. The optical properties of the films were studied by using Absorbance and Transmittance spectrums in wavelength range of (400-1100) nm, the results showed that the electronic transitions is indirect and the energy gap for the SiCN films increase with increasing of nitrog

The present study employed the NAG-4SX3-3D analyzer to precisely measure the energy response of the sensor. The goal was to enhance the understanding of this technology by providing expert information about the device. This technology offers an economical, quick, accurate, and sensitive approach. By utilizing the turbidity method, Cyproheptadine hydrochloride (CPH) was quantified in pharmaceutical samples without the need for additional substances. CPH is expected to undergo a direct reaction with calcium hexacyanoferrate, resulting in the formation of white precipitates. The linear range for CPH measurement falls within the range of (0.008–30) mM. The relative standard deviation (RSD) for six repetitions at concentrations of (6 and

The coordination ability of the azo-Schiff base 2-[1,5-Dimethyl-3-[2-(5-methyl-1H-indol-3-yl)-ethyl imino]-2-phenyl-2,3-dihydro-1H-pyrazol-4-ylazo]-5- hydroxy-benzoic acid has been proven in complexation reactions with Co(II), Ni(II), Cu(II), Pd(II) and Pt(II) ions. The free ligand (LH) and its complexes were characterized using elemental analysis, determination of metal concentration, magnetic susceptibility, molar conductivity, FTIR, Uv-Vis, (1H, 13C) NMR spectra, mass spectra and thermal analysis (TGA). The results confirmed the coordination of the ligand through the nitrogen of the azomethine, Azo group (Azo) and the carboxylate ion with the metal ions. The activation thermodynamic parameters, such as ΔE*, ΔH*, ΔS*, ΔG*and K are cal

In this work, fluid catalytic cracking of vacuum gas oil to produce gasoline over prepared faujasite type Y zeolite was investigated using experimental laboratory plant scale of fluidized bed reactor.
The catalytic activity of prepared faujasite type NaY, NaNH4Y and NaHY zeolites was investigated. The cracking process was carried out in the temperature range 440 to 500 oC, weight hourly space velocity (WHSV) range 10 to 25 h-1 ,and atmospheric pressure . The catalytic activities of the prepared faujasite type NaY , NaNH4Y and NaHY zeolites were determined in terms of vacuum gas oil (VGO) conversion, and gasoline yield . The conversion at 500oC and WHSV10 hr-1 by using faujasite type NaY, NaNH4Y and NaHY zeolite were 50.2%, 64.1% and 6

The purpose of this work is to concurrently estimate the UVvisible spectra of binary combinations of piroxicam and mefenamic acid using the chemometric approach. To create the model, spectral data from 73 samples (with wavelengths between 200 and 400 nm) were employed. A two-layer artificial neural network model was created, with two neurons in the output layer and fourteen neurons in the hidden layer. The model was trained to simulate the concentrations and spectra of piroxicam and mefenamic acid. For piroxicam and mefenamic acid, respectively, the Levenberg-Marquardt algorithm with feed-forward back-propagation learning produced root mean square errors of prediction of 0.1679 μg/mL and 0.1154 μg/mL, with coefficients of determination of

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discrimina