Electronic properties such as density of state, energy gap, HOMO (the highest occupied molecular orbital) level, LUMO (the lowest unoccupied molecular orbital) level and density of bonds, as well as spectroscopic properties like infrared (IR), Raman scattering, force constant, and reduced masses for coronene C₂₄, reduced graphene oxide (rGO) C₂₄O₅and interaction between C₂₄O₅and NO₂gas molecules were investigated. Density functional theory (DFT) with the exchange hybrid function B3LYP with 6-311G** basis sets through the Gaussian 09 W software program was used to do these calculations. Gaussian view 05 was em

A series of batch demulsification runs were carried out to evaluate the final emulsified water content of emulsion samples after the exposure to microwave. An experimental study was conducted to evaluate the effects of a set of operating variables on the demulsification performance. Several microwave irradiation demulsification runs were carried out at different irradiation powers (700, 800, and 900 watt), using water-in-oil emulsion samples containing different water contents (20-80%, 30-70%, and 50-50%) and salt contents (10000, 20000, and 30000 ppm). It was found that the best separation efficiency was obtained at 900watt, 50% water content and 160 s of irradiation time. Experimental results showed that microwave radiation method can

The density functional B3LYP is used to investigate the effect of decorating the silver (Ag) atom on the sensing capability of an AlN nanotube (AlN-NT) in detecting thiophosgene (TP). There is a weak interaction between the pristine AlN-NT and TP with the sensing response (SR) of approximately 9.4. Decoration of the Ag atom into the structure of AlN-NT causes the adsorption energy of TP to decrease from − 6.2 to − 22.5 kcal/mol. Also, the corresponding SR increases significantly to 100.5. Moreover, the recovery time when TP is desorbed from the surface of the Ag-decorated AlN-NT (Ag@AlN-NT) is short, i.e., 24.9 s. The results show that Ag@AlN-NT can selectively detect TP among other gases, such as N2, O2, CO2, CO, and H2O.

In this research, damping properties for composite materials were evaluated using logarithmic decrement method to study the effect of reinforcements on the damping ratio of the epoxy matrix. Three stages of composites were prepared in this research. The first stage included preparing binary blends of epoxy (EP) and different weight percentages of polysulfide rubber (PSR) (0%, 2.5%, 5%, 7.5% and 10%). It was found that the weight percentage 5% of polysulfide was the best percentage, which gives the best mechanical properties for the blend matrix. The advantage of this blend matrix is that; it mediates between the brittle properties of epoxy and the flexible properties of a blend matrix with the highest percentage of PSR. The second stage

Background: The incorporation of rubber has not been entirely successful because it can have detrimental effects on the transverse Strength and hence the rigidity of the denture base. Materials and methods: Zirconium oxide nanoparticales were coated with a layer of trimethoxysilylpropylmethacrylate (TMSPM) before sonication in monomer (MMA) with the percentages 3% by weight then mixed with powder using conventional procedure.(100) samples were prepared and divided into five groups according to the test performed ,Each group consisted of 20 specimens and these were subdivided into 2 groupsGroup (A): control group (10 specimens of high impact acrylic resin without zirconium oxide) and Group (B):zirconium oxide group(10 specimens of high impac

In this investigation, metal matrix composites (MMC_s) were manufactured by using powder technology. Aluminum 6061 is reinforced with two different ceramics particles (SiC and B₄C) with different volume fractions as (3, 6, 9 and 12 wt. %). The most important applications of particulate reinforcement of aluminum matrix are: Pistons, Connecting rods etc. The specimens were prepared by using aluminum powder with 150 µm in particle size and SiC, B₄C powder with 200 µm in particle size. The chosen powders were mixed by using planetary mixing setup at 250 rpm for 4hr.with zinc stearate as an activator material in steel ball milling. After mixing process the powders were compacted by hydraulic

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a