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In this research, gold nanoparticles (Au-NPs) are biosynthesized from tetrachloroaurate (AuCl4−) aqueous solution through a simple and ecofriendly route using water extract of black Camellia sinensis leaf (C. sinensis L.) which acted as a reductant and stabilizer simultaneously. The prepared gold nanoparticles are characterized using UV-visible spectroscopy, X-ray diffraction (XRD), and transmission electron microscopy (TEM). Also, determination of the accurate predictor model for chemical reactions is particularly important because of high cost of the chemical materials and measurement devices. While the artificial neural networks (ANNs) are one of the appropriate tools to forecast any phenomena, due to the low number of data set related to chemical experimental was caused to provide appropriate model is a time-consuming iterative process. With the aim to improve the accuracy of the ANN model and overcome the local convergence of this problem, a global search technique, biogeography-based optimization (BBO) method which integrated by chaotic map is employed. The improved model showed minimum mean squared error (MSE) of 0.0134 and maximum coefficient of determination ( ) equal to 0.9822 compared with several other famous ANN training algorithm, utilizing output experimental data obtained from biosynthesis proceeding.

Computational Modeling of Biosynthesized Gold Nanoparticles in Black Camellia sinensis Leaf Extract