Journal of Advanced Materials in Engineering (Esteghlal)

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Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the time to failure as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of time to failure as a result of SCC is yet to be developed. This paper is an effort to investigate the capability of artificial neural network in estimatiing the time to failure for SCC of 304 stainless steel in aqueous chloride solution and to provide a sensitivity analysis thereof. The input parameters considered are temperature, chloride ion concentration, and applied stress. The time to failure is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show that artificial neural network can predict the time to failure for about 74% of the variance of SCC experimental data. Furthermore, the sensitivity analysis also exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.

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Precipitation has always been one of the important methods in the preparation of ceramic nanopowders. In this study, the most important parameters, ageing time and concentration parameters, have been studied. Yttrium oxide (Yttria) nanopowder was synthesized by precipitation method. Yttria micropowder and ammonium hydrogen carbonate were used as precursor materials. The study involved aging time and concentration in four and three levels, repectively (3, 6, 12 and 24h for ageing time and 0.25, 0.5 and 0.75 mol/L for concentration). Synthesized phases, thermal behavior and particle size were studied by X-ray diffraction pattern (XRD), thermogravimetry (TG), differential thermal analysis (DTA) and field emission scanning electron microscopy (FE-SEM). Fourier transform infrared spectroscopy analysis (FTIR) was used for studying bonding before and after the heat treatment at 900, 1000 and 1100 ^°C.

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Bio-inspired silver nanoparticles were synthesized with the aid of a novel method, using leaves of the plant Nigella sativa. After drying the leaves in air, they were first sweltered in boiling distilled water and the liquid was filtered subsequently. The result was the brothused to reduce solutions including various concentrations of silver nitrate in a proper amount of pH. The displayed UV–visible spectra identified formation of silver nanoparticles whenever the colorless initial acclimated mixture turned brown. The centrifuged powder samples were examined using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (FESEM) and energy dispersive X-ray diffraction analysis (EDX) methods. The results clearly revealed that the final particles of precipitated powder are high purity agglomerates of silver nanoparticles. Besides, the effects of various amounts of the silver salt on particle size of nano silver were studied, using a particle size analyzer. FTIR results also indicated the role of different functional groups in the synthetic process.