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
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/213539
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/213539
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling 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 Técnicas2025-09-29T09:35:52Zoai: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:34982025-09-29 09:35:52.854CONICET 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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score 13.070432

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