Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach

Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach Publisher Pubmed

Paskiabi FA^{1, 2} ; Mirzaei E¹ ; Amani A¹ ; Shokrgozar MA² ; Saber R^{1, 3} ; Faridimajidi R¹

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1. Department of Medical Nanotechnology, School of Advanced Technologies in medicine, Tehran University of Medical Sciences, Tehran, Iran
2. National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, Iran
3. Research Center for Science and Technology in Medicine (RCSTIM), Imam Khomeini Hospital Complex - Keshavarz Blvd., Tehran, Iran

Source: International Journal of Biological Macromolecules Published:2015

Abstract

This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ε-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentration, working distance, drum speed and applied voltage were considered as input and the standard deviation of the angles of nanofibers, introducing fibers alignments, as the output of the model. The results demonstrated that drum speed and applied voltage are two critical factors influencing nanofibers alignment, however their effect are entirely interdependent. Their effects also are not independent of other electrospinning parameters. In obtaining aligned electrospun nanofibers, the concentration and working distance can also be effective. In vitro cell culture study on random and aligned nanofibers showed directional growth of cells on aligned fibers. © 2015 Elsevier B.V.

Style	Citing Format
MLA	Paskiabi FA, et al.. "Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach." International Journal of Biological Macromolecules, vol. 81, no. , 2015, pp. 1089-1097.
APA	Paskiabi FA, Mirzaei E, Amani A, Shokrgozar MA, Saber R, Faridimajidi R (2015). Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach. International Journal of Biological Macromolecules, 81(), 1089-1097.
Chicago	Paskiabi FA, Mirzaei E, Amani A, Shokrgozar MA, Saber R, Faridimajidi R. "Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach." International Journal of Biological Macromolecules 81, no. (2015): 1089-1097.
Harvard	Paskiabi FA et al. (2015) 'Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach', International Journal of Biological Macromolecules, 81(), pp. 1089-1097.
Vancouver	Paskiabi FA, Mirzaei E, Amani A, Shokrgozar MA, Saber R, Faridimajidi R. Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach. International Journal of Biological Macromolecules. 2015;81():1089-1097.
BibTex	@article{ author = {Paskiabi FA and Mirzaei E and Amani A and Shokrgozar MA and Saber R and Faridimajidi R}, title = {Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach}, journal = {International Journal of Biological Macromolecules}, volume = {81}, number = {}, pages = {1089-1097}, year = {2015} }
RIS	TY - JOUR AU - Paskiabi FA AU - Mirzaei E AU - Amani A AU - Shokrgozar MA AU - Saber R AU - Faridimajidi R TI - Optimizing Parameters on Alignment of Pcl/Pga Nanofibrous Scaffold: An Artificial Neural Networks Approach JO - International Journal of Biological Macromolecules VL - 81 IS - SP - 1089 EP - 1097 PY - 2015 ER -

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