Soluble Block Copolyimides

Hloba, N. I.; Прокопчук, Николай Романович; Krut’ko, E. T.; Zelencovsky, V. M.; Sofina, S. Yu; Заиков, Г. Е.

doi:10.1201/b16876-11

Cited by 2 publications

(4 citation statements)

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“…The PDNFM MLP , using four electrospinning parameters as model input variables, is the most accurate model for prediction of the diameter of electrospun PCL/Gt nanobers against the changing electrospinning parameters. Eqn (16) is the logarithmic sigmoid and eqn (17) illustrates the PDNFM MLP in electrospun PCL/Gt nanobers.…”

Section: Prediction Performance Of Mlpmentioning

confidence: 99%

“…This property depends on many processing parameters. 17,24 ANN models have been applied previously as a predictive modeling tool for electrospun nanobers. 52 For example, Ketabchi et al developed and tested the accuracy of the ANN model for predicting the diameter of chitosan/PEO nanobers in trials and studies and for analysis of the interactions between the involved electrospinning parameters and the diameter of the chitosan/PEO nanobers with sufficient sensitivity and specicity.…”

Section: Prediction Performance Of Svmmentioning

confidence: 99%

“…These parameters have a positive impact on downsizing of the nanobers. [14][15][16][17][18] Despite important experimental investigations to determine ber diameter, using scanning electron microscopy, transmission electron microscopy, and atomic force microscopy for example, it is still time-consuming and expensive. 18,19 Furthermore, complexities in the electrospinning method and many factors simultaneously affecting the preparation techniques cause the ndings from statistical tools, such as response surface methodology and regression analysis, to be very noisy.…”

Section: Introductionmentioning

confidence: 99%

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Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(3-caprolactone) (PCL)/gelatin (Gt) nanofibers.Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM MLP is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving.According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM MLP in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning.

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Section: Prediction Performance Of Mlpmentioning

confidence: 99%

Section: Prediction Performance Of Svmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies

Kalantary

Jahani²,

Pourbabaki

et al. 2019

RSC Adv.

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show abstract

“…Water contact angle (WCA) of the membrane surface is dependent on its surface chemistry and microstructure [44]. The WCA of the electrospun membrane is expected to be higher than other fabrication techniques such as phase inversion due to membrane roughness created by the overlapping structure of nanofibers.…”

Section: Membrane Wettability and Porosity (Lep Ca Porosity)mentioning

confidence: 99%