Modeling and optimization of the core-shell nanofibrous composite mat as a scaffold via hybrid models

It has been a challenging subject for researchers to manipulate the electrospinning key factors to achieve a composite nanofiber with proper properties. In this study, an experiment according to central composite design to investigate the effect of parameters including polyvinylpyrrolidone concentration, zeolite concentration, voltage, core flow rate and shell flow rate on diameter, maximum strength and porosity of polyethersulfone/polyvinylpyrrolidone/zeolite core-shell composite nanofiber has been designed. Later on, two sets of models consisting of response surface methodology and artificial neural network are trained. Then, their performance was evaluated based on the definition of a novel goodness function. In the next step, the genetic algorithm is used to find the optimal design for scaffold applications. The results demonstrated that the average goodness value of models based on an artificial neural network ([Formula: see text] 1.999) is higher than response surface methodology ones ([Formula: see text] 1.780). Additionally, the genetic algorithm was able to find an optimal design with lower cost value (0.006) than the optimum sample (0.113) among the produced ones. Finally, the scanning electron microscopy micrographs highlighted that there is a strong and good cell proliferation on the selected design of nanofiber composite mat as the optimum scaffold.