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Title: Evaluation of Effective Electrospinning Parameters Controlling Gelatin Nanofibers Diameter Via Modelling Artificial Neural Networks
Journal: Fibers and Polymers
Author: 1. Majid Naghibzadeh, 2. Mahdi Adabi
Year: 2014
Address: 1. Departments of Nanotechnology, Research and Clinical Center for Infertility, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 2. Departments of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
Abstract: The aim of this work was to evaluate the effective parameters for prediction of the electrospun gelatin nanofibers diameter using artificial neural network (ANN) technique. The various sets of electrospinning process including temperature, applied voltage and polymer and solvent concentrations were designed to produce pure gelatin nanofibers. The obtained results by analyzing Scanning Electron Microscopy (SEM) images indicated that the produced nanofibers diameter was in the range of 85 to 750 nm. Due to the volume of the data, k fold cross-validation method was used for data setting. Data were divided into the five categories and trained and tested using ANN technique. The results indicated that the network including 4 input variables, 3 hidden layers with 10, 18 and 9 nodes in each layers, respectively, and one output layer had the best performance in the testing sets. The mean squared error (MSE) and linear regression (R) between observed and predicted nanofibers diameter were 0.1531 and 0.9424, respectively. The obtained results demonstrated that the selected neural network model had acceptable performance for evaluating involved parameters and prediction of nanofibers diameter.
Keywords: Gelatin, Nanofibers, Electrospinning, Modeling, ANN
Application: Optimizing Electrospinning Parameters
Product Model 1: Electroris
Product Model 2:
URL: https://link.springer.com/article/10.1007/s12221-014-0767-x#="https://link.springer.com" & "/article/10.1007/s12221-014-0767-x"#