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Chitosan/polyethylene oxide (PEO) solution makes electrospun nanofibers with decreased beads and diameters in comparison with lonely chitosan (CS). The aim of this work was to find an artificial neural network (ANN) model for predicting the chitosan/PEO blend electrospun nanofiber diameter. Chitosan/PEO concentration ratio, distance between nozzle tip and collector, applied voltage, and flow rate were considered as input variables, and chitosan/PEO blend electrospun nanofiber diameter was considered as output variable. Scanning electron microscopy images indicated that electrospun nanofiber diameter was approximately 50-185 nm. For increasing validity, k-fold cross validation method was applied to dataset. The ANN technique was used for training and testing via fivefold of dataset. The best results of prediction were obtained via network with three hidden layers including 10, 15, and 5 nodes in each layer, respectively. The mean square error (MSE) and correlation coefficient between the observed and predicted thickness of the nanofibers in the chosen model were about 0.0707 and 0.9630, respectively, indicating the ANN technique validity in the prediction procedure. For the analysis of interactions between the involved electrospinning parameters and nanofiber diameter, 3D graphs in various levels were plotted. In conclusion, the results indicated that using the prediction process via ANN could be relevant in the decision to produce nanofibers with desired shape and diameter via electrospinning. [ABSTRACT FROM AUTHOR]

Abstract: In this research, the polyvinyl alcohol (PVA) nanofibers through forcespinning process were successfully produced and the effective parameters for predicting nanofibers diameter using artificial neural network (ANN) were investigated. The various parameters of forcespinning process including rotational speed, orifice, distance to the collector, and polymer concentration were designed to produce PVA nanofibers. Scanning electron microscopy (SEM) showed that the produced fibers diameter was in the range of 0.56–1.9 μm. The neural network with four input factors, three hidden layers with 5, 10, 1 nodes in each layers, respectively, and one output layer had the best performance in the testing sets. Moreover, the mean squared error (MSE) and linear regression (R) between observed and predicted nanofibers diameter were about 0.1077 and 0.9387, respectively, demonstrating a suitable performance for the prediction of nanofibers diameter using the selected neural network model. [ABSTRACT FROM AUTHOR]