A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers

Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and Works by trial and e...

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Autores:
Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
Fonthal Rico, Faruk
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/15901
Acceso en línea:
https://hdl.handle.net/10614/15901
https://doi.org/10.3390/mi14071410
https://red.uao.edu.co/
Palabra clave:
Artificial neural networks
PEO nanofibers
Electrospinning
Rights
openAccess
License
Derechos reservados - MDPI, 2023
id REPOUAO2_5cc25aba37cb2e84378acda35c7293af
oai_identifier_str oai:red.uao.edu.co:10614/15901
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network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
title A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
spellingShingle A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
Artificial neural networks
PEO nanofibers
Electrospinning
title_short A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
title_full A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
title_fullStr A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
title_full_unstemmed A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
title_sort A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
dc.creator.fl_str_mv Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
Fonthal Rico, Faruk
dc.contributor.author.none.fl_str_mv Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
Fonthal Rico, Faruk
dc.subject.proposal.eng.fl_str_mv Artificial neural networks
PEO nanofibers
Electrospinning
topic Artificial neural networks
PEO nanofibers
Electrospinning
description Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and Works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-11-14T20:23:06Z
dc.date.available.none.fl_str_mv 2024-11-14T20:23:06Z
dc.type.eng.fl_str_mv Artículo de revista
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.eng.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.eng.fl_str_mv Text
dc.type.driver.eng.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.eng.fl_str_mv info:eu-repo/semantics/publishedVersion
format http://purl.org/coar/resource_type/c_2df8fbb1
status_str publishedVersion
dc.identifier.citation.spa.fl_str_mv Solís-Ríos, D., et. al. (2023). A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers. Micromachines. 14(7). 16 p. https://doi.org/10.3390/mi14071410
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/10614/15901
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.3390/mi14071410
dc.identifier.eissn.spa.fl_str_mv 2072666X
dc.identifier.instname.spa.fl_str_mv Universidad Autónoma de Occidente
dc.identifier.reponame.spa.fl_str_mv Respositorio Educativo Digital UAO
dc.identifier.repourl.none.fl_str_mv https://red.uao.edu.co/
identifier_str_mv Solís-Ríos, D., et. al. (2023). A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers. Micromachines. 14(7). 16 p. https://doi.org/10.3390/mi14071410
2072666X
Universidad Autónoma de Occidente
Respositorio Educativo Digital UAO
url https://hdl.handle.net/10614/15901
https://doi.org/10.3390/mi14071410
https://red.uao.edu.co/
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.citationendpage.spa.fl_str_mv 16
dc.relation.citationissue.spa.fl_str_mv 7
dc.relation.citationstartpage.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 14
dc.relation.ispartofjournal.spa.fl_str_mv Micromachines
dc.relation.references.none.fl_str_mv 1. Hong, J.; Yeo, M.; Yang, G.H.; Kim, G. Cell-Electrospinning and Its Application for Tissue Engineering. Int. J. Mol. Sci. 2019, 20, 6208. [CrossRef] [PubMed]
2. Niu, B.; Zhan, L.; Shao, P.; Xiang, N.; Sun, P.; Chen, H.; Gao, H. Electrospinning of Zein-Ethyl Cellulose Hybrid Nanofibers with ImprovedWater Resistance for Food Preservation. Int. J. Biol. Macromol. 2020, 142, 592–599. [CrossRef]
3. Tang, Y.; Cai, Z.; Sun, X.; Chong, C.; Yan, X.; Li, M.; Xu, J. Electrospun Nanofiber-Based Membranes for Water Treatment. Polymers 2022, 14, 2004. [CrossRef]
4. Torres-Martinez, E.J.; Cornejo Bravo, J.M.; Serrano Medina, A.; Pérez González, G.L.; Villarreal Gómez, L.J. A Summary of Electrospun Nanofibers as Drug Delivery System: Drugs Loaded and Biopolymers Used as Matrices. Curr. Drug Deliv. 2018, 15, 1360–1374. [CrossRef] [PubMed]
5. Ma, L.; Deng, L.; Chen, J. Applications of Poly(Ethylene Oxide) in Controlled Release Tablet Systems: A Review. Drug Dev. Ind. Pharm. 2014, 40, 845–851. [CrossRef] [PubMed]
6. Hawkins, B.C.; Burnett, E.; Chou, S.-F. Physicomechanical Properties and in Vitro Release Behaviors of Electro-spun Ibuprofen- Loaded Blend PEO/EC Fibers. Mater. Today Commun. 2022, 30, 103205. [CrossRef] [PubMed]
7. Rubert, M.; Dehli, J.; Li, Y.-F.; Taskin, M.B.; Xu, R.; Besenbacher, F.; Chen, M. Electro-spun PCL/PEO Coaxial Fibers for Basic Fibroblast Growth Factor Delivery. J. Mater. Chem. B 2014, 2, 8538–8546. [CrossRef]
8. Jovanska, L.; Chiu, C.-H.; Yeh, Y.-C.; Chiang, W.-D.; Hsieh, C.-C.; Wang, R. Development of a PCL-PEO Double Network Colorimetric PH Sensor Using Electro-spun Fibers Containing Hibiscus Rosa Sinensis Extract and Silver Nanoparticles for Food Monitoring. Food Chem. 2022, 368, 130813. [CrossRef]
9. Bhattacharya, S.; Roy, I.; Tice, A.; Chapman, C.; Udangawa, R.; Chakrapani, V.; Plawsky, J.L.; Linhardt, R.J. High-Conductivity and High-Capacitance Electro-spun Fibers for Supercapacitor Applications. ACS Appl. Mater. Interfaces 2020, 12, 19369–19376. [CrossRef]
10. de Carvalho, L.D.; Peres, B.U.; Maezono, H.; Shen, Y.; Haapasalo, M.; Jackson, J.; Carvalho, R.M.; Manso, A.P. Doxycycline Release and Antibacterial Activity from PMMA/PEO Electro-spun Fiber Mats. J. Appl. Oral Sci. 2019, 27, e20180663. [CrossRef]
11. El-hadi, A.; Al-Jabri, F. Influence of Electrospinning Parameters on Fiber Diameter and Mechanical Properties of Poly(3- Hydroxybutyrate) (PHB) and Polyanilines (PANI) Blends. Polymers 2016, 8, 97. [CrossRef]
12. Wong, S.-C.; Baji, A.; Leng, S. Effect of Fiber Diameter on Tensile Properties of Electro-spun Poly("-Caprolactone). Polymer 2008, 49, 4713–4722. [CrossRef]
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14. Villarreal-Gómez, L.J.; Cornejo-Bravo, J.M.; Vera-Graziano, R.; Grande, D. Electrospinning as a Powerful Technique for Biomedical Applications: A Critically Selected Survey. J. Biomater. Sci. Polym. Ed. 2016, 27, 157–176. [CrossRef]
15. Pillay, V.; Dott, C.; Choonara, Y.E.; Tyagi, C.; Tomar, L.; Kumar, P.; du Toit, L.C.; Ndesendo, V.M.K. A Review of the Effect of Processing Variables on the Fabrication of Electro-spun Nanofibers for Drug Delivery Applications. J. Nanomater. 2013, 2013, 789289. [CrossRef]
16. Burden, F.; Winkler, D. Bayesian Regularization of Neural Networks. In Artificial Neural Networks; Livingstone, D.J., Ed.; Methods in Molecular BiologyTM; Humana Press: Totowa, NJ, USA, 2008; Volume 458, pp. 23–42, ISBN 978-1-58829-718-1.
17. Sarkar, K.; Ghalia, M.B.;Wu, Z.; Bose, S.C. A Neural Network Model for the Numerical Prediction of the Diameter of Electro-Spun Polyethylene Oxide Nanofibers. J. Mater. Process. Technol. 2009, 209, 3156–3165. [CrossRef]
18. Brooks, H.; Tucker, N. Electrospinning Predictions Using Artificial Neural Networks. Polymer 2015, 58, 22–29. [CrossRef]
19. Ketabchi, N.; Naghibzadeh, M.; Adabi, M.; Esnaashari, S.S.; Faridi-Majidi, R. Preparation and Optimization of Chitosan/ Polyethylene Oxide Nanofiber Diameter Using Artificial Neural Networks. Neural Comput. Appl. 2017, 28, 3131–3143. [CrossRef]
20. Chen, G.; Guo, J.; Nie, J.; Ma, G. Preparation, Characterization, and Application of PEO/HA Core Shell Nanofibers Based on Electric Field Induced Phase Separation during Electrospinning. Polymer 2016, 83, 12–19. [CrossRef]
21. Xu, Y.; Zou, L.; Lu, H.; Wei, Y.; Hua, J.; Chen, S. Preparation and Characterization of Electro-spun PHBV/PEO Mats: The Role of Solvent and PEO Component. J. Mater. Sci. 2016, 51, 5695–5711. [CrossRef]
22. Basu, P.; Repanas, A.; Chatterjee, A.; Glasmacher, B.; NarendraKumar, U.; Manjubala, I. PEO–CMC Blend Nanofibers Fabrication by Electrospinning for Soft Tissue Engineering Applications. Mater. Lett. 2017, 195, 10–13. [CrossRef]
23. Grothe, T.; Groaßerhode, C.; Hauser, T.; Kern, P.; Stute, K.; Ehrmann, A. Needleless Electrospinning of PEO Nanofiber Mats. In Proceedings of the Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017), Beijing, China, 14–16 April 2017; Atlantis Press: Beijing, China, 2017.
24. Yue, T.-T.; Li, X.; Wang, X.-X.; Yan, X.; Yu, M.; Ma, J.-W.; Zhou, Y.; Ramakrishna, S.; Long, Y.-Z. Electrospinning of Carboxymethyl Chitosan/Polyoxyethylene Oxide Nanofibers for Fruit Fresh-Keeping. Nanoscale Res. Lett. 2018, 13, 239. [CrossRef] [PubMed]
25. Togay, ¸S.M.; Bulbul, Y.E.; Tort, S.; Demirta¸s Korkmaz, F.; Acartürk, F.; Dilsiz, N. Fabrication of Doxycycline-Loaded Electro-spun PCL/PEO Membranes for a Potential Drug Delivery System. Int. J. Pharm. 2019, 565, 83–94. [CrossRef] [PubMed]
26. Serôdio, R.; Schickert, S.L.; Costa-Pinto, A.R.; Dias, J.R.; Granja, P.L.; Yang, F.; Oliveira, A.L. Ultrasound Sonication Prior to Electrospinning Tailors Silk Fibroin/PEO Membranes for Periodontal Regeneration. Mater. Sci. Eng. C 2019, 98, 969–981. [CrossRef]
27. ¸Sim¸sek, M.; Aldemir, S.D.; Gümü¸sderelio ˘ glu, M. Anticellular PEO Coatings on Titanium Surfaces by Sequential Electrospinning and Crosslinking Processes. Emergent Mater. 2019, 2, 169–179. [CrossRef]
28. Amiri, N.; Ajami, S.; Shahroodi, A.; Jannatabadi, N.; Amiri Darban, S.; Fazly Bazzaz, B.S.; Pishavar, E.; Kalalinia, F.; Movaffagh, J. Teicoplanin-Loaded Chitosan-PEO Nanofibers for Local Antibiotic Delivery and Wound Healing. Int. J. Biol. Macromol. 2020, 162, 645–656. [CrossRef]
29. Bulbul, Y.E.; Okur, M.; Demirtas-Korkmaz, F.; Dilsiz, N. Development of PCL/PEO Electro-spun Fibrous Membranes Blended with Silane-Modified Halloysite Nanotube as a Curcumin Release System. Appl. Clay Sci. 2020, 186, 105430. [CrossRef]
30. Cardenas Bates, I.I.; Loranger, É.; Chabot, B. Chitosan-PEO Nanofiber Mats for Copper Removal in Aqueous Solution Using a New Versatile Electrospinning Collector. SN Appl. Sci. 2020, 2, 1540. [CrossRef]
31. Govindasamy, K.; Dahlan, N.A.; Janarthanan, P.; Goh, K.L.; Chai, S.-P.; Pasbakhsh, P. Electro-spun Chitosan/Polyethylene-Oxide (PEO)/Halloysites (HAL) Membranes for Bone Regeneration Applications. Appl. Clay Sci. 2020, 190, 105601. [CrossRef]
32. Hernández-Martínez, D.; Nicho, M.E.; Alvarado-Tenorio, G.; García-Carvajal, S.; Castillo-Ortega, M.M.; Vásquez-López, C. Elaboration and Characterization of P3HT–PEO–SWCNT Fibers by Electrospinning Technique. SN Appl. Sci. 2020, 2, 462. [CrossRef]
33. Zaitoon, A.; Lim, L.-T. Effect of Poly(Ethylene Oxide) on the Electrospinning Behavior and Characteristics of Ethyl Cellulose Composite Fibers. Materialia 2020, 10, 100649. [CrossRef]
34. Darbasizadeh, B.; Mortazavi, S.A.; Kobarfard, F.; Jaafari, M.R.; Hashemi, A.; Farhadnejad, H.; Feyzi-barnaji, B. Electro-spun Doxorubicin-Loaded PEO/PCL Core/Sheath Nanofibers for Chemopreventive Action against Breast Cancer Cells. J. Drug Deliv. Sci. Technol. 2021, 64, 102576. [CrossRef]
35. Kharat, Z.; Sadri, M.; Kabiri, M. Herbal Extract Loaded Chitosan/PEO Nanocomposites as Antibacterial Coatings of Orthopaedic Implants. Fibers Polym. 2021, 22, 989–999. [CrossRef]
36. Pereao, O.; Uche, C.; Bublikov, P.S.; Bode-Aluko, C.; Rossouw, A.; Vinogradov, I.I.; Nechaev, A.N.; Opeolu, B.; Petrik, L. Chitosan/PEO Nanofibers Electro-spun on Metallized Track-Etched Membranes: Fabrication and Characterization. Mater. Today Chem. 2021, 20, 100416. [CrossRef]
37. Zheng, G.; Peng, H.; Jiang, J.; Kang, G.; Liu, J.; Zheng, J.; Liu, Y. Surface Functionalization of PEO Nanofibers Using a TiO2 Suspension as Sheath Fluid in a Modified Coaxial Electrospinning Process. Chem. Res. Chin. Univ. 2021, 37, 571–577. [CrossRef]
38. Goncalves, A.; Ray, P.; Soper, B.; Stevens, J.; Coyle, L.; Sales, A.P. Generation and Evaluation of Synthetic Patient Data. BMC Med. Res. Methodol. 2020, 20, 108. [CrossRef] [PubMed]
39. Le, T.A.; Baydin, A.G.; Zinkov, R.; Wood, F. Using Synthetic Data to Train Neural Networks Is Model-Based Reasoning. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 14–19 May 2017. [CrossRef]
40. Medeiros, G.B.; Lima, F.D.A.; De Almeida, D.S.; Guerra, V.G.; Aguiar, M.L. Modification and Functionalization of Fibers Formed by Electrospinning: A Review. Membranes 2022, 12, 861. [CrossRef]
41. Álvarez-Suarez, A.S.; López-Maldonado, E.A.; Graeve, O.A.; Martinez-Pallares, F.; Gómez-Pineda, L.E.; Oropeza-Guzmán, M.T.; Iglesias, A.L.; Ng, T.; Serena-Gómez, E.; Villarreal-Gómez, L.J. Fabrication of Porous Polymeric Structures Using a Simple Sonication Technique for Tissue Engineering. J. Polym. Eng. 2017, 37, 943–951. [CrossRef]
42. Gutiérrez González, J.; Fernández Leyes, M.D.; Ritacco, H.A.; Schroeder, W.F.; Zucchi, I.A. Long PEO-Based Nano-ribbons Generated in a Polystyrene Matrix through Reaction-Induced Microphase Separation Followed by a Fast Crystallization Process. Soft Matter 2021, 17, 2279–2289. [CrossRef]
43. Rabbi, A.; Nasouri, K.; Bahrambeygi, H.; Shoushtari, A.M.; Babaei, M.R. RSM and ANN Approaches for Modeling and Optimizing of Electro-spun Polyurethane Nanofibers Morphology. Fibers Polym. 2012, 13, 1007–1014. [CrossRef]
44. Karimi, M.A.; Pourhakkak, P.; Adabi, M.; Firoozi, S.; Adabi, M.; Naghibzadeh, M. Using an Artificial Neural Network for the Evaluation of the Parameters Controlling PVA/Chitosan Electro-spun Nanofibers Diameter. e-Polymers 2015, 15, 127–138. [CrossRef]
45. Khatti, T.; Naderi-Manesh, H.; Kalantar, S.M. Application of ANN and RSM Techniques for Modeling Electrospinning Process of Polycaprolactone. Neural Comput. Appl. 2019, 31, 239–248. [CrossRef]
dc.rights.spa.fl_str_mv Derechos reservados - MDPI, 2023
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rights_invalid_str_mv Derechos reservados - MDPI, 2023
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spelling Solis-Rios, DanielVillarreal-Gómez, Luis JesúsGoyes, Clara EugeniaCornejo-Bravo, José ManuelFong-Mata, María BereniceCalderón Arenas, Jorge MarioMartínez Rincón, Harold AlbertoMejía-Medina, David AbdelFonthal Rico, Farukvirtual::5790-12024-11-14T20:23:06Z2024-11-14T20:23:06Z2023Solís-Ríos, D., et. al. (2023). A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers. Micromachines. 14(7). 16 p. https://doi.org/10.3390/mi14071410https://hdl.handle.net/10614/15901https://doi.org/10.3390/mi140714102072666XUniversidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and Works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted16 páginasapplication/pdfengMDPIBasel, SwitzerlandDerechos reservados - MDPI, 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2A neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibersArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85167114Micromachines1. Hong, J.; Yeo, M.; Yang, G.H.; Kim, G. Cell-Electrospinning and Its Application for Tissue Engineering. Int. J. Mol. Sci. 2019, 20, 6208. [CrossRef] [PubMed]2. Niu, B.; Zhan, L.; Shao, P.; Xiang, N.; Sun, P.; Chen, H.; Gao, H. Electrospinning of Zein-Ethyl Cellulose Hybrid Nanofibers with ImprovedWater Resistance for Food Preservation. Int. J. Biol. Macromol. 2020, 142, 592–599. [CrossRef]3. Tang, Y.; Cai, Z.; Sun, X.; Chong, C.; Yan, X.; Li, M.; Xu, J. Electrospun Nanofiber-Based Membranes for Water Treatment. Polymers 2022, 14, 2004. [CrossRef]4. Torres-Martinez, E.J.; Cornejo Bravo, J.M.; Serrano Medina, A.; Pérez González, G.L.; Villarreal Gómez, L.J. A Summary of Electrospun Nanofibers as Drug Delivery System: Drugs Loaded and Biopolymers Used as Matrices. Curr. Drug Deliv. 2018, 15, 1360–1374. [CrossRef] [PubMed]5. Ma, L.; Deng, L.; Chen, J. Applications of Poly(Ethylene Oxide) in Controlled Release Tablet Systems: A Review. Drug Dev. Ind. Pharm. 2014, 40, 845–851. [CrossRef] [PubMed]6. Hawkins, B.C.; Burnett, E.; Chou, S.-F. Physicomechanical Properties and in Vitro Release Behaviors of Electro-spun Ibuprofen- Loaded Blend PEO/EC Fibers. Mater. Today Commun. 2022, 30, 103205. [CrossRef] [PubMed]7. Rubert, M.; Dehli, J.; Li, Y.-F.; Taskin, M.B.; Xu, R.; Besenbacher, F.; Chen, M. Electro-spun PCL/PEO Coaxial Fibers for Basic Fibroblast Growth Factor Delivery. J. Mater. Chem. B 2014, 2, 8538–8546. [CrossRef]8. Jovanska, L.; Chiu, C.-H.; Yeh, Y.-C.; Chiang, W.-D.; Hsieh, C.-C.; Wang, R. Development of a PCL-PEO Double Network Colorimetric PH Sensor Using Electro-spun Fibers Containing Hibiscus Rosa Sinensis Extract and Silver Nanoparticles for Food Monitoring. Food Chem. 2022, 368, 130813. [CrossRef]9. Bhattacharya, S.; Roy, I.; Tice, A.; Chapman, C.; Udangawa, R.; Chakrapani, V.; Plawsky, J.L.; Linhardt, R.J. High-Conductivity and High-Capacitance Electro-spun Fibers for Supercapacitor Applications. ACS Appl. Mater. Interfaces 2020, 12, 19369–19376. [CrossRef]10. de Carvalho, L.D.; Peres, B.U.; Maezono, H.; Shen, Y.; Haapasalo, M.; Jackson, J.; Carvalho, R.M.; Manso, A.P. Doxycycline Release and Antibacterial Activity from PMMA/PEO Electro-spun Fiber Mats. J. Appl. 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