The aim of this study was to develop a sensor based on a carbon paste electrodes (CPEs) modified with used MIP for determination of organophosphorus pesticides (OPPs). The modified electrode exhibited a significantly increased sensitivity and selectivity of (OPPs). The MIP was prepared by thermo-polymerization method using N,N-diethylaminoethymethacrylate (NNDAA) as functional monomer, N,N-1,4-phenylenediacrylamide (NNPDA) as cross-linker, the acetonitrile used as solvent and (Opps) as the template molecule. The three OPPs (diazinon, quinalphos and chlorpyrifos) were chosen as the templates, which have been selected as base analytes which used widely in agriculture sector. The extraction efficiency of the imprinted polymers has been evaluated by various parameters affecting to optimize the selective pre-concentration of OPPs from aqueous samples. The extraction efficiency of the MIPs-OPPs from environmental water samples was evaluated using carbon paste electrode and analytical parameters of the method, the slopes, linearity and detection limits of the liquid electrodes were ranged from 24.8 – 31.3 mV/decade , (10-1 - 10-7 ) mg L-1and (2.2-8.2x10-6) mg L-1, respectively with correlation coefficient (r) (0.9991-0.9998) and repeatability were established. The method was validated and successfully applied to determined OPPs compounds from environmental water samples.
By using governing differential equation and the Rayleigh-Ritz method of minimizing the total potential energy of a thermoelastic structural system of isotropic thermoelastic thin plates, thermal buckling equations were established for rectangular plate with different fixing edge conditions and with different aspect ratio. The strain energy stored in a plate element due to bending, mid-plane thermal force and thermal bending was obtained. Three types of thermal distribution have been considered these are: uniform temperature, linear distribution and non-linear thermal distribution across thickness. It is observed that the buckling strength enhanced considerably by additional clamping of edges. Also, the thermal buckling temperatures and
... Show MoreIn this work, an inventive photovoltaic evaporative cooling (PV/EC) hybrid system was constructed and experimentally investigated. The PV/EC hybrid system has the prosperous advantage of producing electrical energy and cooling the PV panel besides providing cooled-humid air. Two cooling techniques were utilized: backside evaporative cooling (case #1) and combined backside evaporative cooling with a front-side water spray technique (case #2). The water spraying on the front side of the PV panel is intermittent to minimize water and power consumption depending on the PV panel temperature. In addition, two pad thicknesses of 5 cm and 10 cm were investigated at three different water flow rates of 1, 2, and 3 lpm. In Case #1,
... Show MoreOne of the most important challenges facing the designers of the sewerage system is the corrosion of sewers due to the influence of sewerage contaminates which lead to failure of the main lines of sewers. In this study, a reference mix of 1: 1.5: 3 was used and the 4% Flocrete PC200 by weight of cement was added to the same mixing ratio in the second mixture. Twenty-four samples were tested for each mixture, 12 of which were used to compression strength test in ages (7, 14 and 28) day and six samples were submerged after 28 days of wet treatment at (5 and 10) % concentrations of sulfuric acid. The other six samples were painted after 28 days of wet treatment with coating Polyurethane and after 24 hours were flooded with a concentrat
... Show MoreThe deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
... Show MoreTo accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens
... Show MoreIn the present research, the nuclear deformation of the Ne, Mg, Si, S, Ar, and Kr even–even isotopes has been investigated within the framework of Hartree–Fock–Bogoliubov method and SLy4 Skyrme parameterization. In particular, the deform shapes of the effect of nucleons collective motion by coupling between the single-particle motion and the potential surface have been studied. Furthermore, binding energy, the single-particle nuclear density distributions, the corresponding nuclear radii, and quadrupole deformation parameter have been also calculated and compared with the available experimental data. From the outcome of our investigation, it is possible to conclude that the deforming effects cannot be neglected in a characterization o
... Show More???? ?? ??? ????? ???? ?????? ?????????? ????? ??????? ???? ?????? ????? ??? ??? ????? ?? ???? ??? ????? ????? ???? ????? ????? ?? 0-3cm, 10cm, 20cm, 30cm, 40cm ???????? ????? ?? ???? ????? ???????? ?? ???? ????? ?????? CR-39??????? ?? ??? ??? ?????????? ???????????? ???????? ???? n.cm-2.s-1 5 x 103?? ?????? ?????????? Am241- Be??? ???? ??????? ????????? ??? ?? ???? ????? ?????????? ??? ?? ????? ??????? ?????? 0.881±0.086??? ?? ??????? ????? ??? ????? ??? ?? ????? ????? ??? ???????? ???0.441±0.036 ??? ?? ???????