A novel formula derived by using ABC algorithm for calculation of the average fiber diameter of electrospun poly (Ԑ-caprolactone) scaffolds

Year 2021, , 301 - 308, 15.08.2021

Çağdaş Yılmaz , Didem Demir , Nimet Bölgen Karagülle , Ali Akdağlı

https://doi.org/10.35860/iarej.832439

Cited By: 1

Abstract

The characteristics of a scaffold that is the basic component of tissue engineering are considerably influenced by the fiber diameter of the fibrous scaffolds. Since the significant effect of the fiber diameter on the scaffold properties, many researchers have focused on estimating the fiber diameter based on the electrospinning parameters. With similar motivation, in this paper, a new and simple closed-form expression, which can help researchers in fabricating the electrospun poly (ԑ-caprolactone) (PCL) scaffold with desired fiber diameter, is presented. In order to construct the expression proposed, an experimental study has been performed to obtain the data set, in which 25 experimental data including average fiber diameter (AFD) values dependent on different combinations of parameters such as voltage, solution concentration, tip to collector (TTC) distance, and flow rate. Then, an expression has been constructed that is used to estimate the AFD of the electrospun PCL, and the coefficients of the expression were determined by using the artificial bee colony (ABC) algorithm. In order to validate the estimation ability of the expression, the metrics such as mean absolute error (MAE) and mean absolute percentage error (MAPE) have been used, and the optimization and test errors were respectively obtained as 3.30% and 1.27% in terms of MAPE. In addition, the results obtained were compared with those reported in the literature. Results show that our new expression can be successfully used to estimate the AFD of electrospun PCL prior to the electrospinning process. Thus, the number of test repetitions could be reduced by using the expression proposed, and time, cost, and labor could be saved in this way. This study contributes to the literature because there have been only a limited number of studies that focus on estimating the AFD of PCL nanofiber despite many studies about various polymers.

Keywords

ABC Algorithm, Electrospinning, Fiber Diameter, Optimization, PCL

Supporting Institution

Scientific Research Projects Unit of Mersin University

Project Number

2018-1-AP2-2785

Thanks

This work was supported by the Scientific Research Projects Unit of Mersin University (2018-1-AP2-2785).

References

• 1. Mishra, R. K., et al., Electrospinning production of nanofibrous membranes. Environmental Chemistry Letters, 2019. 17(2): p. 767-800.
• 2. Bölgen, N., D. Demir, and A. Vaseashta, Nanofibers for the Detection of VOCs, in Nanoscience Advances in CBRN Agents Detection, Information and Energy Security. 2015, Springer: Dordrecht. p. 159-165.
• 3. Amariei, N., et al., The Influence of Polymer Solution on the Properties of Electrospun 3D Nanostructures. IOP Conference Series: Materials Science and Engineering, 2017. 209: p. 12092-12100.
• 4. Angel, N., et al., Effect of Processing Parameters on the Electrospinning of Cellulose Acetate Studied by Response Surface Methodology. Journal of Agriculture and Food Research, 2019. 2.
• 5. Haider, A., S. Haider, and I.-K. Kang, A comprehensive review summarizing the effect of electrospinning parameters and potential applications of nanofibers in biomedical and biotechnology. Arabian Journal of Chemistry, 2018. 11(8): p. 1165-1188.
• 6. Serbezeanu, D., et al., Preparation and characterization of thermally stable polyimide membranes by electrospinning for protective clothing applications. Textile Research Journal, 2015. 85(17): p. 1763-1775.
• 7. Zhao, G., et al., Piezoelectric polyacrylonitrile nanofiber film-based dual-function self-powered flexible sensor. ACS Applied Materials & Interfaces, 2018. 10(18): p. 15855-15863.
• 8. Diez-Pascual, A. and A. Díez-Vicente, Antimicrobial and sustainable food packaging based on poly(butylene adipate-co-terephthalate) and electrospun chitosan nanofibers. RSC Adv., 2015. 5(113): p. 93095-93107.
• 9. Faccini, M., et al., Electrospun Carbon Nanofiber Membranes for Filtration of Nanoparticles from Water. Journal of Nanomaterials, 2015. 2015.
• 10. Demir, D., et al., Magnetic nanoparticle-loaded electrospun poly(ε-caprolactone) nanofibers for drug delivery applications. Applied Nanoscience, 2018. 8(6): p. 1461-1469.
• 11. İşoğlu, İ. A., et al., Stem cells combined 3D electrospun nanofibrous and macrochannelled matrices: a preliminary approach in repair of rat cranial bones. Artificial Cells, Nanomedicine, and Biotechnology, 2019. 47(1): p. 1094-1100.
• 12. Bölgen, N., S. Ceylan, and D. Demir, Influence of fabrication temperature on the structural features of chitosan gels for tissue engineering applications. International Advanced Researches and Engineering Journal, 2019. 3(3): p. 170-174.
• 13. Hong, Y., Electrospun Fibrous Polyurethane Scaffolds in Tissue Engineering, in Advances in polyurethane biomaterials. 2016, Woodhead. p. 543-559.
• 14. Wu, J. and Y. Hong, Enhancing cell infiltration of electrospun fibrous scaffolds in tissue regeneration. Bioactive Materials, 2016. 1(1): p. 56-64.
• 15. Maleki, H., et al., The influence of process parameters on the properties of electrospun PLLA yarns studied by the response surface methodology. Journal of Applied Polymer Science, 2015. 132(5).
• 16. Amiri, N., et al., Modeling and process optimization of electrospinning of chitosan-collagen nanofiber by response surface methodology. Materials Research Express, 2018. 5(4).
• 17. Tang, Z. S., et al., Response Surface Modeling of Electrospinning Parameters on Titanium Oxide Nanofibers’ Diameter: A Box-Behnken Design (BBD). Advanced Science Letters, 2017. 23(11): p. 11237-11241.
• 18. Maurya, A., et al., Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks. Journal of Electrostatics, 2020. 104.
• 19. Naghibzadeh, M. and M. Adabi, Evaluation of effective electrospinning parameters controlling gelatin nanofibers diameter via modelling artificial neural networks. Fibers and Polymers, 2014. 15(4): p. 767-777.
• 20. Nurwaha, D. and X.J.G.J.T.O. Wang, Modeling and Prediction of Electrospun Fiber Morphology using Artificial Intelligence Techniques. Global Journal of Technology & Optimization, 2019. 10(1): p. 237-243.
• 21. Khatti, T., et al., Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone. Neural Computing & Applications, 2019. 31(1): p. 239-248.
• 22. Abolhasani, M.M., et al., Towards predicting the piezoelectricity and physiochemical properties of the electrospun P (VDF-TrFE) nanogenrators using an artificial neural network. Polymer Testing, 2018. 66: p. 178-188.
• 23. Esnaashari, S.S., et al., Evaluation of the effective electrospinning parameters controlling Kefiran nanofibers diameter using modelling artificial neural networks. Nanomedicine research journal, 2017. 2(4): p. 239-249.
• 24. Naghibzadeh, M., et al., Evaluation of the effective forcespinning parameters controlling polyvinyl alcohol nanofibers diameter using artificial neural network. Advances in polymer technology, 2018. 37(6): p. 1608-1617.
• 25. Shahrabi, S.S., J. Barzin, and P.J.M.R.E. Shokrollahi, Statistical approach to estimate fiber diameter of PET/PVP blend electrospun using Taguchi method and fitting regression model. Materials research express, 2018. 6(2).
• 26. Karaboga, D., An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report - TR06. Technical Report, Erciyes University, 2005.
• 27. Biçer, M. and A. Akdagli, An experimental study on microwave imaging of breast cancer with the use of tumor phantom. Applied Computational Electromagnetics Society Journal, 2017. 32(10): p. 941-947.
• 28. Hetmaniok, E., D. Słota, and A. Zielonka, Restoration of the cooling conditions in a three-dimensional continuous casting process using artificial intelligence algorithms. Applied Mathematical Modelling, 2015. 39(16): p. 4797-4807.
• 29. Li, G., et al., Artificial bee colony algorithm with gene recombination for numerical function optimization. Applied Soft Computing, 2017. 52: p. 146-159.
• 30. Ustun, D. and A. Akdagli, Design of a dual-wideband monopole antenna by artificial bee colony algorithm for UMTS, WLAN, and WiMAX applications. International Journal of Microwave and Wireless Technologies, 2017. 9(5): p. 1197-1208.
• 31. Zhao, H. and S. Yin, Inverse analysis of geomechanical parameters by the artificial bee colony algorithm and multi-output support vector machine. Inverse Problems in Science and Engineering, 2016. 24(7): p. 1266-1281.
• 32. Karaboga, D., et al., A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 2014. 42(1): p. 21-57.
• 33. Rad, Z.P., et al., Fabrication and characterization of PCL/zein/gum arabic electrospun nanocomposite scaffold for skin tissue engineering. Materials Science and Engineering, 2018. 93: p. 356-366.
• 34. Bölgen, N., et al., In vitro and in vivo degradation of non-woven materials made of poly(ε-caprolactone) nanofibers prepared by electrospinning under different conditions. Journal of Biomaterials Science, Polymer Edition, 2005. 16(12): p. 1537-1555.