Evaluation of effective needleless electrospinning parameters controlling polyacrylonitrile nanofibers diameter via modeling artificial neural networks

摘要

In this study, we aimed to predict the effects of different needleless electrospinning parameters on the diameter of polyacrylonitrile (PAN) nanofibers via artificial neural network method. The various factors, including polymer solution concentration, applied voltage, and spinneret-to-collector distance, were designed to investigate the diameter of PAN nanofibers produced via needleless electrospinning system. Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient back-propagation algorithms were used for the analysis. The results indicated that PAN nanofiber diameters had a direct correlation with the polymer solution concentration and applied voltage and an inverse relation with the nozzle-to-collector distance. The Pearson correlation coefficients were significant at .01% level for all prediction models. The Bayesian regularization mode I achieved the highest regression value (.9944) for the test data set. The regression value was also calculated and the maximum regression value (.9936) was obtained for the Bayesian regularization model I.