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Cited 15 time in webofscience Cited 18 time in scopus
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Modeling tensile strength and suture retention of polycaprolactone electrospun nanofibrous scaffolds by artificial neural networks

Authors
Reddy, B. S.In, Kim HongPanigrahi, Bharat B.Paturi, Uma Maheswera ReddyCho, K. K.Reddy, N. S.
Issue Date
Mar-2021
Publisher
ELSEVIER
Keywords
Electrospinning; Polycaprolactone nanofibers; Artificial neural networks; Tensile strength; Suture strength
Citation
MATERIALS TODAY COMMUNICATIONS, v.26
Indexed
SCIE
SCOPUS
Journal Title
MATERIALS TODAY COMMUNICATIONS
Volume
26
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/4064
DOI
10.1016/j.mtcomm.2021.102115
ISSN
2352-4928
2352-4928
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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공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
공학계열 > Dept.of Materials Engineering and Convergence Technology > Journal Articles

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Cho, Kwon Koo
대학원 (나노신소재융합공학과)
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