First, polystyrene (PS) was fractionated using solvent-precipitation methods; then the chemical modification of PS was investigated in the presence of Lewis acid [BF3·O(C2H5)2] catalyst with some anhydrides such as phthalic anhydride, glutaric anhydride, maleic anhydride, and propionic anhydride. The amount of the polyfunctional groups bonded to polymer structures changed depending on the molecular weight of the polymer, and more functional groups connected to the highest-molecular-weight fraction. The amount of the bonded functional groups was determined with the chemical analysis method. The structures of PS functionalized with anhydrides were confirmed by FTIR, 1H NMR, and thermogravimetric studies. Their adhesion capability and corrosion resistance against metals have been investigated under various conditions. The results showed that the addition of functional groups to PS increases its adhesion capability and corrosion resistance. This increase is attributed to the quantity of the functional groups bonded to PS. 相似文献
The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.
The fluorine doped cadmium oxide (CdO:F) samples have been deposited at 250 °C by ultrasonic spray pyrolysis method. Cadmiumacetat-dihydrat and ammonium fluoride have been taken as a source of cadmium and fluorine-dopant respectively. The thickness of the CdO:F samples was about 1.4 μm. X-ray diffraction pattern of the CdO:F samples has revealed that the samples are polycrystalline with cubic sodium chloride structure. There are shifts of the d values (interplanar spacing) for CdO:F samples with respect to standard CdO film. The lattice parameters for cubic structure have been calculated using the Bragg equation. The texture coefficients calculated for various planes at different fluorine concentrations indicate that the samples have exhibited (111) and (200) preferential orientations. 相似文献