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Usage of artificial neural network for estimating of the electrospun nanofiber diameter

At the present time, nanomaterials are used in the medicine and biology applications such as drug and gene delivery, bio-detection of pathogens, MRI contrast enhancement, tumor destruction via heating, and protein detection. Tissue engineering which is another of these applications is being increasingly popular. Because extracellular matrix (ECM) consists nano-sized structure; the usage of nanomaterials in tissue engineering enables to produce of tissue scaffolds that are more closely resemble the ECM form. Thus, the success rate increases in tissue engineering as it is provided a more favorable environment for the growth of cells. Electrospinning is a popular method among nanomaterial production ones. The diameter of the fiber produced by electrospinning technique depends on the various parameters like process, solution, and environmental parameters. In this study, an ANN model based on multilayer perceptron (MLP) is presented for predicting the average fiber diameter (AFD) of electrospun gelatin/bioactive glass (Gt/BG) scaffold. The experimental results previously published in the literature, which include one solution parameter (BG content) as well as two process parameters (tip to collector distance and solution flow rate) related to producing of electrospun Gt/BG nanofiber, have been used. The values of average percentage error between the predicted average fiber diameters and experimental ones are achieved as 3.27 %. The results obtained from the proposed model have also been confirmed by comparing with results of AFD expression reported elsewhere. It is illustrated that the AFD of electrospun Gt/BG can be accurately predicted by the model proposed here without requiring any complicated or sophisticated knowledge of the mathematical and physical background.