Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine l...
- Autores
- Trupp, Federico Javier; Cibils, Roberto Manuel; Goyanes, Silvia Nair
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Electrospinning is one of the leading techniques for fiber development. Still, one of the biggest challenges of the technique is to control the nanofiber morphology without many trial-and-error tests. In this study, it is demonstrated that via design of experiments (DoE), response surface methodology (RSM) and machine learning regressions (MLR) it is possible to predict the beads-on-string size, size distribution and bead density in electrospun poly(vinylidene fluoride) (PVDF) mats with a small number of tests. PVDF concentration, dimethylacetamide/acetone ratio, tip-to-collector voltage and distance were the parameters considered for the design. The results show good agreement between the experimental and modeled data. It was found that concentration and solvent ratio play the main roles in minimizing bead size and number, distance tends to reduce them, and voltage does not play a significant role. As an evaluation of the potential of the method, bead-free fibers were obtained through the predicted parameter values. Comparison of the performance of the two methods is presented for the first time in electrospinning research. Response surface methodology resulted much faster, but MLR achieved a lower error and better generalization abilities. This approach and the availability of the MLR script used in this work may help other groups implement it in their research and find information hidden in the data while improving model prediction performance.
Fil: Trupp, Federico Javier. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Cibils, Roberto Manuel. Invap S. E.; Argentina
Fil: Goyanes, Silvia Nair. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina - Materia
-
BEAD FORMATION
ELECTROSPINNING
MACHINE LEARNING REGRESSIONS
POLY(VINYLIDENE FLUORIDE)
PREDICTION AND OPTIMIZATION
RESPONSE SURFACE METHODOLOGY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/213539
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CONICET Digital (CONICET) |
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Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressionsTrupp, Federico JavierCibils, Roberto ManuelGoyanes, Silvia NairBEAD FORMATIONELECTROSPINNINGMACHINE LEARNING REGRESSIONSPOLY(VINYLIDENE FLUORIDE)PREDICTION AND OPTIMIZATIONRESPONSE SURFACE METHODOLOGYhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Electrospinning is one of the leading techniques for fiber development. Still, one of the biggest challenges of the technique is to control the nanofiber morphology without many trial-and-error tests. In this study, it is demonstrated that via design of experiments (DoE), response surface methodology (RSM) and machine learning regressions (MLR) it is possible to predict the beads-on-string size, size distribution and bead density in electrospun poly(vinylidene fluoride) (PVDF) mats with a small number of tests. PVDF concentration, dimethylacetamide/acetone ratio, tip-to-collector voltage and distance were the parameters considered for the design. The results show good agreement between the experimental and modeled data. It was found that concentration and solvent ratio play the main roles in minimizing bead size and number, distance tends to reduce them, and voltage does not play a significant role. As an evaluation of the potential of the method, bead-free fibers were obtained through the predicted parameter values. Comparison of the performance of the two methods is presented for the first time in electrospinning research. Response surface methodology resulted much faster, but MLR achieved a lower error and better generalization abilities. This approach and the availability of the MLR script used in this work may help other groups implement it in their research and find information hidden in the data while improving model prediction performance.Fil: Trupp, Federico Javier. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Cibils, Roberto Manuel. Invap S. E.; ArgentinaFil: Goyanes, Silvia Nair. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaSAGE Publications2022-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213539Trupp, Federico Javier; Cibils, Roberto Manuel; Goyanes, Silvia Nair; Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions; SAGE Publications; Journal Of Industrial Textiles; 51; 5_suppl; 6-2022; 9071S-9096S1528-0837CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.sagepub.com/doi/10.1177/15280837221106235info:eu-repo/semantics/altIdentifier/doi/10.1177/15280837221106235info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:22Zoai:ri.conicet.gov.ar:11336/213539instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:23.058CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
title |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
spellingShingle |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions Trupp, Federico Javier BEAD FORMATION ELECTROSPINNING MACHINE LEARNING REGRESSIONS POLY(VINYLIDENE FLUORIDE) PREDICTION AND OPTIMIZATION RESPONSE SURFACE METHODOLOGY |
title_short |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
title_full |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
title_fullStr |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
title_full_unstemmed |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
title_sort |
Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions |
dc.creator.none.fl_str_mv |
Trupp, Federico Javier Cibils, Roberto Manuel Goyanes, Silvia Nair |
author |
Trupp, Federico Javier |
author_facet |
Trupp, Federico Javier Cibils, Roberto Manuel Goyanes, Silvia Nair |
author_role |
author |
author2 |
Cibils, Roberto Manuel Goyanes, Silvia Nair |
author2_role |
author author |
dc.subject.none.fl_str_mv |
BEAD FORMATION ELECTROSPINNING MACHINE LEARNING REGRESSIONS POLY(VINYLIDENE FLUORIDE) PREDICTION AND OPTIMIZATION RESPONSE SURFACE METHODOLOGY |
topic |
BEAD FORMATION ELECTROSPINNING MACHINE LEARNING REGRESSIONS POLY(VINYLIDENE FLUORIDE) PREDICTION AND OPTIMIZATION RESPONSE SURFACE METHODOLOGY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Electrospinning is one of the leading techniques for fiber development. Still, one of the biggest challenges of the technique is to control the nanofiber morphology without many trial-and-error tests. In this study, it is demonstrated that via design of experiments (DoE), response surface methodology (RSM) and machine learning regressions (MLR) it is possible to predict the beads-on-string size, size distribution and bead density in electrospun poly(vinylidene fluoride) (PVDF) mats with a small number of tests. PVDF concentration, dimethylacetamide/acetone ratio, tip-to-collector voltage and distance were the parameters considered for the design. The results show good agreement between the experimental and modeled data. It was found that concentration and solvent ratio play the main roles in minimizing bead size and number, distance tends to reduce them, and voltage does not play a significant role. As an evaluation of the potential of the method, bead-free fibers were obtained through the predicted parameter values. Comparison of the performance of the two methods is presented for the first time in electrospinning research. Response surface methodology resulted much faster, but MLR achieved a lower error and better generalization abilities. This approach and the availability of the MLR script used in this work may help other groups implement it in their research and find information hidden in the data while improving model prediction performance. Fil: Trupp, Federico Javier. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina Fil: Cibils, Roberto Manuel. Invap S. E.; Argentina Fil: Goyanes, Silvia Nair. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Polímeros y Materiales Compuestos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina |
description |
Electrospinning is one of the leading techniques for fiber development. Still, one of the biggest challenges of the technique is to control the nanofiber morphology without many trial-and-error tests. In this study, it is demonstrated that via design of experiments (DoE), response surface methodology (RSM) and machine learning regressions (MLR) it is possible to predict the beads-on-string size, size distribution and bead density in electrospun poly(vinylidene fluoride) (PVDF) mats with a small number of tests. PVDF concentration, dimethylacetamide/acetone ratio, tip-to-collector voltage and distance were the parameters considered for the design. The results show good agreement between the experimental and modeled data. It was found that concentration and solvent ratio play the main roles in minimizing bead size and number, distance tends to reduce them, and voltage does not play a significant role. As an evaluation of the potential of the method, bead-free fibers were obtained through the predicted parameter values. Comparison of the performance of the two methods is presented for the first time in electrospinning research. Response surface methodology resulted much faster, but MLR achieved a lower error and better generalization abilities. This approach and the availability of the MLR script used in this work may help other groups implement it in their research and find information hidden in the data while improving model prediction performance. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/213539 Trupp, Federico Javier; Cibils, Roberto Manuel; Goyanes, Silvia Nair; Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions; SAGE Publications; Journal Of Industrial Textiles; 51; 5_suppl; 6-2022; 9071S-9096S 1528-0837 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/213539 |
identifier_str_mv |
Trupp, Federico Javier; Cibils, Roberto Manuel; Goyanes, Silvia Nair; Poly(vinylidene fluoride) electrospun nonwovens morphology: Prediction and optimization of the size and number of beads on fibers through response surface methodology and machine learning regressions; SAGE Publications; Journal Of Industrial Textiles; 51; 5_suppl; 6-2022; 9071S-9096S 1528-0837 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://journals.sagepub.com/doi/10.1177/15280837221106235 info:eu-repo/semantics/altIdentifier/doi/10.1177/15280837221106235 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
SAGE Publications |
publisher.none.fl_str_mv |
SAGE Publications |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1842268727264935936 |
score |
13.13397 |