Modeling tensile strength and suture retention of polycaprolactone electrospun nanofibrous scaffolds by artificial neural networks

Reddy, B.S. and In, Kim Hong and Panigrahi, Bharat B. and Paturi, Uma Maheswera Reddy and Cho, K.K. and Reddy, N.S. (2021) Modeling tensile strength and suture retention of polycaprolactone electrospun nanofibrous scaffolds by artificial neural networks. Materials Today Communications, 26. p. 102115. ISSN 23524928

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Abstract

Electrospun polycaprolactone (PCL) scaffolds are broadly used in tissue engineering applications due to their superior biomechanical properties and compatibility with the cell matrix. The properties of PCL scaffolds depend on electrospinning parameters. The relationships between electrospinning process parameters and scaffold properties are complicated and nonlinear. In this study, we used the artificial neural networks (ANN) technique to estimate the tensile strength and suture retention of PCL scaffolds as a function of electrospinning parameters (polymer concentration, solution feed rate, applied voltage, and nozzle to collector distance). A standalone ANN software was developed, and the predicted properties were a good agreement with the experimental data. The present model has excellent learning precision for both training and testing data sets. The precise predictions revealed that the model could estimate the relationships between electrospinning parameters and properties of PCL scaffolds adequately.

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IITH Creators:
IITH CreatorsORCiD
Panigrahi, Bharat BhooshanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Biomechanical properties; Electrospinning parameters; Electrospinning process; Nanofibrous scaffolds; Polycaprolactone scaffolds; Polymer concentrations; Tissue engineering applications; Training and testing
Subjects: Others > Metallurgy Metallurgical Engineering
Materials Engineering > Materials engineering
Divisions: Department of Material Science Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 27 Jul 2021 04:51
Last Modified: 04 Mar 2022 04:42
URI: http://raiith.iith.ac.in/id/eprint/8535
Publisher URL: http://doi.org/10.1016/j.mtcomm.2021.102115
OA policy: https://v2.sherpa.ac.uk/id/publication/35836
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