Preparation and artificial neural networks analysis of ultrafine β-Sialon powders by microwave-assisted carbothermal reduction nitridation of sol–gel derived powder precursors

Abstract

β-Sialon powders were synthesized by microwave-assisted carbothermal reduction of powder materials resultant from a sol–gel process using Si(OC2H5)4, Al(NO3)3·9H2O and sucrose (C12H22O11) as the main starting materials and artificial neural networks (ANNs) was applied to model and predict relative contents of β-Sialon in the final product samples. β-Sialon was formed at as low as 1250°C by using the technique developed with the present work. Furthermore, addition of Fe2O3 promoted the β-Sialon formation. As-prepared β-Sialon ultrafine powders were granular with primary size of about 69nm. A back propagation (BP) ANNs was used to establish a model to predict the reaction extents (relative contents of β-Sialon) under various processing conditions. The results indicated that the BP ANNs could be effectively used to establish the nonlinear relationships between the relative contents of β-Sialon and the processing conditions.