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Abstract
Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers’ diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R2 = 0.959) in comparison with MLR results (R2 = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM.
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Details
1 Tehran University of Medical Sciences, Department of Occupational Health Engineering, School of Public Health, Tehran, Iran (GRID:grid.411705.6) (ISNI:0000 0001 0166 0922)
2 College of Environment, Department of Natural Environment and Biodiversity, Faculty of Environment, Karaj, Iran (GRID:grid.411705.6)
3 Shahid Beheshti University of Medical Sciences, Department of Toxicology and Pharmacology, School of Pharmacy, Tehran, Iran (GRID:grid.411600.2)