Prediction of Nanofiber Diameter and Optimization of Electrospinning Process via Response Surface Methodology

Abstract

Response surface methodology (RSM) was used to obtain a more systematic understanding of the electrospinning conditions of polyamide 6 solutions. This method was used to establish a quantitative basis for the relationships between the electrospinning parameters such as applied electric field, the polymer concentrations, the rate of injection and nozzle-collector distance with the diameter of the produced nanofibers, and to predict the optimum conditions for electrospinning to produce nanofibers with controlled size. A response function was empirically determined by central composite design (CCD) using fiber diameter as an observed response and the electrospinning parameters as variables. The relationship between the response and the variables is visualized by a response surface or contour plots. The study of the graphical representations of contour plots, prediction formulas and prediction profiler can predict the operating conditions necessary to generate nanofibers with the desired diameters.

Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon‐6,6

“…Nanofibers of nylon‐6,6 were prepared using the electrospinning method as described previously 12. Briefly, plastic syringes fitted with metal needles were used as the polymer solution reservoir and nozzle for electrospinning.…”

“…INForm v4.0 (Intelligensys, UK), as a commercial ANNs software was used in this study to model the relations between inputs and outputs. From our previous study,12 four factors, namely the polymer concentration (% wt/v), the applied voltage (kV), the working distance (cm), and the rate of injection (mL/h), were considered as input variables of the ANNs and the average of the nanofibers diameter was chosen as the output.…”

“…Due to high level of complexity in electrospinning process,10 the one‐factor‐at‐a‐time approach to recognize the relations between electrospinning parameters and the size/morphology is not only a time‐consuming approach but also probably inefficient way. Thus, many reports have used statistical techniques to investigate the parameters of the process on the size and morphology of the obtained nanofibers 11–15…”

Using an artificial neural network for the evaluation of the parameters controlling PVA/chitosan electrospun nanofibers diameter

“…Different methods are used for predicting the diameter of the produced nanofibers via electrospinning such as response surface methodology (RSM) (14) and artificial neural network (ANN) modeling (15). However, ANN may be more applicable owing to its greater efficiency.…”

Evaluation of Chitosan-Tripolyphosphate Nanoparticles as a p-shRNA Delivery Vector: Formulation, Optimization and Cellular Uptake Study

“…RSM has advantageous properties such as reducing the number of experimental trials, evaluating the interactions among multiple factors, as well as being highly effective and precise, and these characteristics make it an excellent tool for the purpose of optimization. 30–33 …”