NDLI: Evaluation of effective electrospinning parameters controlling gelatin nanofibers diameter via modelling artificial neural networks

Content Provider	Springer Nature Link
Author	Naghibzadeh, Majid Adabi, Mahdi
Copyright Year	2014
Abstract	The aim of this work was to evaluate the effective parameters for prediction of the electrospun gelatin nanofibers diameter using artificial neural network (ANN) technique. The various sets of electrospinning process including temperature, applied voltage and polymer and solvent concentrations were designed to produce pure gelatin nanofibers. The obtained results by analyzing Scanning Electron Microscopy (SEM) images indicated that the produced nanofibers diameter was in the range of 85 to 750 nm. Due to the volume of the data, k fold cross-validation method was used for data setting. Data were divided into the five categories and trained and tested using ANN technique. The results indicated that the network including 4 input variables, 3 hidden layers with 10, 18 and 9 nodes in each layers, respectively, and one output layer had the best performance in the testing sets. The mean squared error (MSE) and linear regression (R) between observed and predicted nanofibers diameter were 0.1531 and 0.9424, respectively. The obtained results demonstrated that the selected neural network model had acceptable performance for evaluating involved parameters and prediction of nanofibers diameter.
Starting Page	767
Ending Page	777
Page Count	11
File Format	PDF
ISSN	12299197
Journal	Fibers and Polymers
Volume Number	15
Issue Number	4
e-ISSN	18750052
Language	English
Publisher	The Korean Fiber Society
Publisher Date	2014-05-31
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gelatin Nanofibers Electrospinning Modeling ANN Polymer Sciences
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering Polymers and Plastics

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