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Title: Using an Artificial Neural Network for the Evaluation of the Parameters Controlling Pva/Chitosan Electrospun Nanofibers Diameter
Journal: e-Polymers
Author: 1. Mohammad Ali Karimi, Pouran Pourhakkak, 2. Mahdi Adabi, Saman Firoozi, 3. Mohsen Adabi, 4. Majid Naghibzadeh
Year: 2015
Address: 1. Department of Chemistry, Payame Noor University, P.O. Box 19395-4697, Tehran, Iran 2. Department of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; 3. Faculty of Engineering, Department of Metallurgy and Materials Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Tehran, Iran 4. Department of Nanotechnology, Research and Clinical Center for Infertility, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract: The purpose of this study was to investigate the validity of an artificial neural network (ANN) method in the prediction of nanofiber diameter to assess the parameters involved in controlling fiber form and thickness. A mixture of polymers including poly(vinyl alcohol) (PVA) and chitosan (CS) at different ratios was chosen as the nanofiber base material. The various samples of nanofibers were fabricated as training and testing datasets for ANN modeling. Different networks of ANN were designed to achieve the purposes of this study. The best network had three hidden layers with 8, 16 and 5 nodes in each layer, respectively. The mean squared error and correlation coefficient between the observed and the predicted diameter of the fibers in the selected model were equal to 0.09008 and 0.93866, respectively, proving the efficacy of the ANN technique in the prediction process. Finally, three-dimensional graphs of the electrospinning parameters involved and nanofiber diameter were plotted to scrutinize the implications.
Keywords: ANN; electrospinning; modeling; nanofibers; PVA/Chitosan.
Application: Optimizing Electrospinning Parameters
Product Model 1: Electroris
Product Model 2:
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