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Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils

In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions....

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Published in:Materials 2023-08, Vol.16 (16), p.5720
Main Authors: Cuahuizo-Huitzil, Guadalupe, Olivares-Xometl, Octavio, Eugenia Castro, María, Arellanes-Lozada, Paulina, Meléndez-Bustamante, Francisco J., Pineda Torres, Ivo Humberto, Santacruz-Vázquez, Claudia, Santacruz-Vázquez, Verónica
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creator Cuahuizo-Huitzil, Guadalupe
Olivares-Xometl, Octavio
Eugenia Castro, María
Arellanes-Lozada, Paulina
Meléndez-Bustamante, Francisco J.
Pineda Torres, Ivo Humberto
Santacruz-Vázquez, Claudia
Santacruz-Vázquez, Verónica
description In the present work, different configurations of nt iartificial neural networks (ANNs) were analyzed in order to predict the experimental diameter of nanofibers produced by means of the electrospinning process and employing polyvinyl alcohol (PVA), PVA/chitosan (CS) and PVA/aloe vera (Av) solutions. In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. It was found that an ANN configuration having more than three hidden layers did not improve the prediction of the experimental diameter of synthesized nanofibers.
doi_str_mv 10.3390/ma16165720
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In addition, gelatin type A (GT)/alpha-tocopherol (α-TOC), PVA/olive oil (OO), PVA/orange essential oil (OEO), and PVA/anise oil (AO) emulsions were used. The experimental diameters of the nanofibers electrospun from the different tested systems were obtained using scanning electron microscopy (SEM) and ranged from 93.52 nm to 352.1 nm. Of the three studied ANNs, the one that displayed the best prediction results was the one with three hidden layers with the flow rate, voltage, viscosity, and conductivity variables. The calculation error between the experimental and calculated diameters was 3.79%. Additionally, the correlation coefficient (R2) was identified as a function of the ANN configuration, obtaining values of 0.96, 0.98, and 0.98 for one, two, and three hidden layer(s), respectively. 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subjects Algorithms
Artificial neural networks
Back propagation
Biopolymers
Chitosan
Configurations
Correlation coefficients
Electrospinning
Emulsions
Essential oils
Gelatin
Mathematical analysis
Mathematical models
Nanofibers
Neural networks
Neurons
Oils & fats
Olive oil
Physical properties
Polyvinyl alcohol
Scanning electron microscopy
Software
Synthesis
Tocopherol
Variables
Viscosity
title Artificial Neural Networks for Predicting the Diameter of Electrospun Nanofibers Synthesized from Solutions/Emulsions of Biopolymers and Oils
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