Yıl 2024,
Cilt: 8 Sayı: 1, 182 - 193, 18.07.2024
Deniz Efendioğlu
,
Şerife Akkoyun
Kaynakça
- Acatay, K. (2004). Generation of superhydrophobic surfaces by electrospinning process (Doctoral dissertation)
DOI: https://research.sabanciuniv.edu/id/eprint/8213
- Ahmadipourroudposht, M., Fallahiarezoudar, E., Yusof, N. M., & Idris, A. (2015). Application of response surface
methodology in optimization of electrospinning process to fabricate (ferrofluid/polyvinyl alcohol) magnetic
nanofibers. Materials Science and Engineering: C, 50, 234-241. DOI: https://doi.org/10.1016/j.msec.2015.02.008
- Akkoyun S., Öktem N., (2021), Effect of viscoelasticity in polymer nanofiber electrospinning: Simulation using
FENE-CR model. Engineering Science and Technology, an International Journal, 24(3), 620-630
DOI: https://doi.org/10.1016/j.jestch.2020.12.017
- Amariei, N., Manea, L. R., Bertea, A. P., Bertea, A., & Popa, A. (2017, June). The influence of polymer solution
on the properties of electrospun 3D nanostructures. In IOP conference series: Materials science and
engineering (Vol. 209, No. 1, p. 012092). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/209/1/012092
- Amiri, N., Moradi, A., Tabasi, S. A. S., & Movaffagh, J. (2018). Modeling and process optimization of
electrospinning of chitosan-collagen nanofiber by response surface methodology. Materials Research
Express, 5(4), 045404. DOI: https://doi.org/10.1088/2053-1591/aaba1d
- Fatile, B. O., Pugh, M., & Medraj, M. (2021). Optimization of the Electrospun Niobium–Tungsten Oxide
Nanofibers Diameter Using Response Surface Methodology. Nanomaterials, 11(7), 1644.
DOI: https://doi.org/10.3390/nano11071644
- Filip, P., & Peer, P. (2019). Characterization of poly (ethylene oxide) nanofibers—Mutual relations between mean
diameter of electrospun nanofibers and solution characteristics. Processes, 7(12), 948.
DOI: https://doi.org/10.3390/pr7120948
- He, H., Wang, Y., Farkas, B., Nagy, Z. K., & Molnar, K. (2020). Analysis and prediction of the diameter and
orientation of AC electrospun nanofibers by response surface methodology. Materials & Design, 194, 108902.
DOI: https://doi.org/10.1016/j.matdes.2020.108902
- Kalantary, S., Jahani, A., & Jahani, R. (2020). MLR and Ann approaches for prediction of synthetic/natural
nanofibers diameter in the environmental and medical applications. Scientific Reports, 10(1), 1-10.
DOI: https://doi.org/10.1038/s41598-020-65121-x
- Kalantary, S., Jahani, A., Pourbabaki, R., & Beigzadeh, Z. (2019). Application of ANN modeling techniques in
the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC
advances, 9(43), 24858-24874. DOI: https://doi.org/10.1039/C9RA04927D
- Ketabchi, N., Naghibzadeh, M., Adabi, M., Esnaashari, S. S., & Faridi-Majidi, R. (2017). Preparation and
optimization of chitosan/polyethylene oxide nanofiber diameter using artificial neural networks. Neural
Computing and Applications, 28(11), 3131-3143. DOI: https://doi.org/10.1007/s00521-016-2212-0
- Khalili, S., Khorasani, S. N., Saadatkish, N., & Khoshakhlagh, K. (2016). Characterization of gelatin/cellulose
acetate nanofibrous scaffolds: Prediction and optimization by response surface methodology and artificial neural
networks. Polymer Science Series A, 58(3), 399-408. DOI: https://doi.org/10.1134/S0965545X16030093
- Naderi, N., Agend, F., Faridi-Majidi, R., Sharifi-Sanjani, N., & Madani, M. (2008). Prediction of nanofiber
diameter and optimization of electrospinning process via response surface methodology. Journal of nanoscience
and nanotechnology, 8(5), 2509-2515. DOI: https://doi.org/10.1166/jnn.2008.536
- Nasouri, K., Bahrambeygi, H., Rabbi, A., Shoushtari, A. M., & Kaflou, A. (2012). Modeling and optimization of
electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks. Journal
of Applied Polymer Science, 126(1), 127-135. DOI: https://doi.org/10.1002/app.36726
- Sukigara, S., Gandhi, M., Ayutsede, J., Micklus, M., & Ko, F. (2004). Regeneration of Bombyx mori silk by
electrospinning. Part 2. Process optimization and empirical modeling using response surface
methodology. Polymer, 45(11), 3701-3708. DOI: https://doi.org/10.1016/j.polymer.2004.03.059
- Thompson, C. J., Chase, G. G., Yarin, A. L., & Reneker, D. H. (2007). Effects of parameters on nanofiber diameter
determined from electrospinning model. Polymer, 48(23), 6913-6922.
DOI: https://doi.org/10.1016/j.polymer.2007.09.017
- Zeraati, M., Pourmohamad, R., Baghchi, B., Chauhan, N. P. S., & Sargazi, G. (2021). Optimization and predictive
modelling for the diameter of nylon-6, 6 nanofibers via electrospinning for coronavirus face masks. Journal of
Saudi Chemical Society, 25(11), 101348. DOI: https://doi.org/10.1016/j.jscs.2021.101348
Optimization of nanofiber diameter in the electrospinning of polyamide 6 by two-level factorial design
Yıl 2024,
Cilt: 8 Sayı: 1, 182 - 193, 18.07.2024
Deniz Efendioğlu
,
Şerife Akkoyun
Öz
The utilization of 2-level factorial design has been extensive in the literature to observe the relationship between parameters and responses. Among the subjects open for exploration, the process of nanofiber creation stands out as an intriguing avenue to explore the correlations that emerge between variables and outcomes. The primary objective of the study is to establish the relationships between the parameters of electrospinning of polyamide 6 (PA6) solutions to obtain desired nanofiber diameters by response surface method (RSM) and two level full factorial design. The investigation hones in on four critical parameters related to the electrospinning of PA6 solutions. These parameters encompass factors like solution concentration, applied voltage, distance between the spinneret and the collector, and the flow rate of the solution. Employing a two-level factorial design, these parameters are methodically manipulated at two distinct levels each to systematically unravel their individual and collective impacts on nanofiber diameter outcomes. To understand the relationship between electrospinning process and these factors, these kind of experimental studies gives us much accurate results.
Etik Beyan
Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz
ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun
davrandığımı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak
gösterdiğimi ve bu kaynaklara kaynakçada yer verdiğimi; kullanılan verilerde herhangi bir
değişiklik yapmadığımı, çalışmanın Committee on Publication Ethics (COPE)' in tüm şartlarını
ve koşullarını kabul ederek etik görev ve sorumluluklara riayet ettiğimi beyan ederim.
Herhangi bir zamanda, çalışmayla ilgili yaptığım bu beyana aykırı bir durumun
saptanması durumunda, ortaya çıkacak tüm ahlaki ve hukuki sonuçlara razı olduğumu
bildiririm
Kaynakça
- Acatay, K. (2004). Generation of superhydrophobic surfaces by electrospinning process (Doctoral dissertation)
DOI: https://research.sabanciuniv.edu/id/eprint/8213
- Ahmadipourroudposht, M., Fallahiarezoudar, E., Yusof, N. M., & Idris, A. (2015). Application of response surface
methodology in optimization of electrospinning process to fabricate (ferrofluid/polyvinyl alcohol) magnetic
nanofibers. Materials Science and Engineering: C, 50, 234-241. DOI: https://doi.org/10.1016/j.msec.2015.02.008
- Akkoyun S., Öktem N., (2021), Effect of viscoelasticity in polymer nanofiber electrospinning: Simulation using
FENE-CR model. Engineering Science and Technology, an International Journal, 24(3), 620-630
DOI: https://doi.org/10.1016/j.jestch.2020.12.017
- Amariei, N., Manea, L. R., Bertea, A. P., Bertea, A., & Popa, A. (2017, June). The influence of polymer solution
on the properties of electrospun 3D nanostructures. In IOP conference series: Materials science and
engineering (Vol. 209, No. 1, p. 012092). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/209/1/012092
- Amiri, N., Moradi, A., Tabasi, S. A. S., & Movaffagh, J. (2018). Modeling and process optimization of
electrospinning of chitosan-collagen nanofiber by response surface methodology. Materials Research
Express, 5(4), 045404. DOI: https://doi.org/10.1088/2053-1591/aaba1d
- Fatile, B. O., Pugh, M., & Medraj, M. (2021). Optimization of the Electrospun Niobium–Tungsten Oxide
Nanofibers Diameter Using Response Surface Methodology. Nanomaterials, 11(7), 1644.
DOI: https://doi.org/10.3390/nano11071644
- Filip, P., & Peer, P. (2019). Characterization of poly (ethylene oxide) nanofibers—Mutual relations between mean
diameter of electrospun nanofibers and solution characteristics. Processes, 7(12), 948.
DOI: https://doi.org/10.3390/pr7120948
- He, H., Wang, Y., Farkas, B., Nagy, Z. K., & Molnar, K. (2020). Analysis and prediction of the diameter and
orientation of AC electrospun nanofibers by response surface methodology. Materials & Design, 194, 108902.
DOI: https://doi.org/10.1016/j.matdes.2020.108902
- Kalantary, S., Jahani, A., & Jahani, R. (2020). MLR and Ann approaches for prediction of synthetic/natural
nanofibers diameter in the environmental and medical applications. Scientific Reports, 10(1), 1-10.
DOI: https://doi.org/10.1038/s41598-020-65121-x
- Kalantary, S., Jahani, A., Pourbabaki, R., & Beigzadeh, Z. (2019). Application of ANN modeling techniques in
the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC
advances, 9(43), 24858-24874. DOI: https://doi.org/10.1039/C9RA04927D
- Ketabchi, N., Naghibzadeh, M., Adabi, M., Esnaashari, S. S., & Faridi-Majidi, R. (2017). Preparation and
optimization of chitosan/polyethylene oxide nanofiber diameter using artificial neural networks. Neural
Computing and Applications, 28(11), 3131-3143. DOI: https://doi.org/10.1007/s00521-016-2212-0
- Khalili, S., Khorasani, S. N., Saadatkish, N., & Khoshakhlagh, K. (2016). Characterization of gelatin/cellulose
acetate nanofibrous scaffolds: Prediction and optimization by response surface methodology and artificial neural
networks. Polymer Science Series A, 58(3), 399-408. DOI: https://doi.org/10.1134/S0965545X16030093
- Naderi, N., Agend, F., Faridi-Majidi, R., Sharifi-Sanjani, N., & Madani, M. (2008). Prediction of nanofiber
diameter and optimization of electrospinning process via response surface methodology. Journal of nanoscience
and nanotechnology, 8(5), 2509-2515. DOI: https://doi.org/10.1166/jnn.2008.536
- Nasouri, K., Bahrambeygi, H., Rabbi, A., Shoushtari, A. M., & Kaflou, A. (2012). Modeling and optimization of
electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks. Journal
of Applied Polymer Science, 126(1), 127-135. DOI: https://doi.org/10.1002/app.36726
- Sukigara, S., Gandhi, M., Ayutsede, J., Micklus, M., & Ko, F. (2004). Regeneration of Bombyx mori silk by
electrospinning. Part 2. Process optimization and empirical modeling using response surface
methodology. Polymer, 45(11), 3701-3708. DOI: https://doi.org/10.1016/j.polymer.2004.03.059
- Thompson, C. J., Chase, G. G., Yarin, A. L., & Reneker, D. H. (2007). Effects of parameters on nanofiber diameter
determined from electrospinning model. Polymer, 48(23), 6913-6922.
DOI: https://doi.org/10.1016/j.polymer.2007.09.017
- Zeraati, M., Pourmohamad, R., Baghchi, B., Chauhan, N. P. S., & Sargazi, G. (2021). Optimization and predictive
modelling for the diameter of nylon-6, 6 nanofibers via electrospinning for coronavirus face masks. Journal of
Saudi Chemical Society, 25(11), 101348. DOI: https://doi.org/10.1016/j.jscs.2021.101348