Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Khanlou, Hossein Mohammad; Sadollah, Ali; Ang, Bee Chin; Kim, Joong Hoon; Talebian, Sepehr; Ghadimi, Azadeh

doi:10.1007/s00521-014-1554-8

Cited by 67 publications

(31 citation statements)

References 30 publications

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“…To date, ANNs have been developed in several studies, particularly in the prediction of electrospun nanofiber diameter (15)(16)(17). For example, RSM and ANN were used for analyzing the morphology of nanofibers synthesized by electrospinning.…”

Section: Introductionmentioning

confidence: 99%

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

Karimi

Pourhakkak

Adabi³

et al. 2015

E-Polymers

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The purpose of this study was to investigate the validity of an artificial neural network (ANN) method in the prediction of nanofiber diameter to assess the parameters involved in controlling fiber form and thickness. A mixture of polymers including poly(vinyl alcohol) (PVA) and chitosan (CS) at different ratios was chosen as the nanofiber base material. The various samples of nanofibers were fabricated as training and testing datasets for ANN modeling. Different networks of ANN were designed to achieve the purposes of this study. The best network had three hidden layers with 8, 16 and 5 nodes in each layer, respectively. The mean squared error and correlation coefficient between the observed and the predicted diameter of the fibers in the selected model were equal to 0.09008 and 0.93866, respectively, proving the efficacy of the ANN technique in the prediction process. Finally, three-dimensional graphs of the electrospinning parameters involved and nanofiber diameter were plotted to scrutinize the implications.

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Section: Introductionmentioning

confidence: 99%

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

Karimi

Pourhakkak

Adabi³

et al. 2015

E-Polymers

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“…Indeed, the polymer solution properties and the electrospinning processing parameters are of vital importance for the surface and mechanical properties of the electrospun fibers, as well as for their uniformity. 101 Figures 14A and 14B show the morphology of the obtained electrospun fiber mats after visualization by scanning electron microscopy (SEM). We can observe that the mats mainly consist in fibers with diameters in the micrometer range.…”

Section: Preparation and Characterization Of The Electrospun Plga Fibersmentioning

confidence: 99%

Gene delivery using dendrimer/pDNA complexes immobilized in electrospun fibers using the Layer-by-Layer technique

et al. 2016

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“…Sarkar et al and Klanlou et al trained artificial neural networks (ANNs) to predict fibre diameters for PEO and poly(methyl methacrylate) (PMMA) respectively [46,47]. Sarkar was able to predict fibre diameters over several datasets taken from literature; however this data contained a priori solution measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Nasouri et al used a response surface methodology (RSM) and artificial neural networks (ANNs) to predict the highest production rate for PAN nanofibres within 4% of the measured value [48]. In all cases where RSM and ANNs were compared the authors noted ANNs were better able to predict electrospinning characteristics [47,48].…”

Section: Introductionmentioning

confidence: 99%

Electrospinning predictions using artificial neural networks

Brooks

Tucker

2015

Polymer

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Abstract:Electrospinning is a relatively simple method of producing nanofibres. Currently there is no method to predict the characteristics of electrospun fibres for a wide range of polymer/solvent combinations and concentrations without first measuring a number of solution properties. This paper shows how artificial neural networks can be trained to make electrospinning predictions using only commonly available prior knowledge of the polymer and solvent. Firstly, a probabilistic neural network was trained to predict the classification of three possibilities; no fibres (electrospraying), beaded fibres and smooth fibres with over 80% correct predictions. Secondly, a generalised neural network was trained to predict fibre diameter with an average absolute percentage error of 22.3% for the validation data. These predictive tools can be used to reduce the parameter space prior to scoping exercises. Graphical Abstract:Predicted versus measured electrospun-fibre diameter from an artificial neural network trained using data from 13 different polymer/solvent combinations from over 20 independent studies.

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Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

Cited by 67 publications

References 30 publications

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

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

Gene delivery using dendrimer/pDNA complexes immobilized in electrospun fibers using the Layer-by-Layer technique

Electrospinning predictions using artificial neural networks

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