Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes

In this project, a simple, cost-effective and scalable solution to improve the mechanical properties of poly(butylene succinate-co- butylene adipate) (PBSA) is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT percentages w/w (0.15, 0.25, 0.5, 0.65, 0.75, 0.85...

ver descrição completa

Detalhes bibliográficos
Autores: Diez-Pascual, Ana María, Champa-Bujaico, Elisabeth, Garcia Díaz, Pilar, Sesini, Valentina, G. Mosquera, Marta E.
Formato: conjunto de datos
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consorcio Madroño
Repositorio:e-cienciaDatos, Repositorio de Datos del Consorcio Madroño
OAI Identifier:doi:10.21950/AN5SP2
Acesso em linha:https://doi.org/10.21950/AN5SP2
Access Level:acceso abierto
Palavra-chave:Chemistry
Machine learning
Mechanical properties
Carbon nanotubes
Poly[(butylene succinate)-co-adipate]
Optimization
id ES_0a8496c9b72cfe9714bb32809f2354eb
oai_identifier_str doi:10.21950/AN5SP2
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubesDiez-Pascual, Ana MaríaChampa-Bujaico, ElisabethGarcia Díaz, PilarSesini, ValentinaG. Mosquera, Marta E.ChemistryMachine learningMechanical propertiesCarbon nanotubesPoly[(butylene succinate)-co-adipate]OptimizationIn this project, a simple, cost-effective and scalable solution to improve the mechanical properties of poly(butylene succinate-co- butylene adipate) (PBSA) is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT percentages w/w (0.15, 0.25, 0.5, 0.65, 0.75, 0.85 and 1.0) have been incorporated in the PBSA matrix via simple solution casting, and the ultrasonication conditions, namely amplitude (A) and time (t) have been optimized to attain a homogenous SWCNT dispersion. The nanocomposites have been characterized in detail by scanning electron microscopy (SEM), Infrared spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile and impact strength tests. Unprecedented increments in stiffness, up to 114 % for the nanocomposite with 0.65 wt% content,were found. Further, four machine learning (ML) algorithms were applied to predict their mechanical properties and very good correlation was attained.e-cienciaDatosDiez-Pascual, Ana María2024info:eu-repo/semantics/datasetinfo:eu-repo/semantics/publishedVersionimage/pngimage/pngimage/pngimage/pngimage/pngimage/pngimage/jpegimage/pngimage/pngimage/pngimage/pngimage/pngimage/pngimage/pngtext/plaintext/plaintext/plainhttps://doi.org/10.21950/AN5SP2reponame:e-cienciaDatos, Repositorio de Datos del Consorcio Madroñoinstname:Consorcio MadroñoInglésinfo:eu-repo/grantAgreement/MICINN//PID2021-122708OB-C31info:eu-repo/grantAgreement/European Community/RYC2021-033921-I/info:eu-repo/grantAgreement/University of Alcalá/PIUAH23%2FCC-046/info:eu-repo/semantics/openAccessCC-BY-NC-ND-4.0doi:10.21950/AN5SP22026-05-29T06:25:11Z
dc.title.none.fl_str_mv Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
title Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
spellingShingle Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
Diez-Pascual, Ana María
Chemistry
Machine learning
Mechanical properties
Carbon nanotubes
Poly[(butylene succinate)-co-adipate]
Optimization
title_short Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
title_full Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
title_fullStr Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
title_full_unstemmed Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
title_sort Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
dc.creator.none.fl_str_mv Diez-Pascual, Ana María
Champa-Bujaico, Elisabeth
Garcia Díaz, Pilar
Sesini, Valentina
G. Mosquera, Marta E.
author Diez-Pascual, Ana María
author_facet Diez-Pascual, Ana María
Champa-Bujaico, Elisabeth
Garcia Díaz, Pilar
Sesini, Valentina
G. Mosquera, Marta E.
author_role author
author2 Champa-Bujaico, Elisabeth
Garcia Díaz, Pilar
Sesini, Valentina
G. Mosquera, Marta E.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Diez-Pascual, Ana María
dc.subject.none.fl_str_mv Chemistry
Machine learning
Mechanical properties
Carbon nanotubes
Poly[(butylene succinate)-co-adipate]
Optimization
topic Chemistry
Machine learning
Mechanical properties
Carbon nanotubes
Poly[(butylene succinate)-co-adipate]
Optimization
description In this project, a simple, cost-effective and scalable solution to improve the mechanical properties of poly(butylene succinate-co- butylene adipate) (PBSA) is reported by using functionalized single-walled carbon nanotubes (SWCNTs). Different SWCNT percentages w/w (0.15, 0.25, 0.5, 0.65, 0.75, 0.85 and 1.0) have been incorporated in the PBSA matrix via simple solution casting, and the ultrasonication conditions, namely amplitude (A) and time (t) have been optimized to attain a homogenous SWCNT dispersion. The nanocomposites have been characterized in detail by scanning electron microscopy (SEM), Infrared spectroscopy, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), tensile and impact strength tests. Unprecedented increments in stiffness, up to 114 % for the nanocomposite with 0.65 wt% content,were found. Further, four machine learning (ML) algorithms were applied to predict their mechanical properties and very good correlation was attained.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
info:eu-repo/semantics/publishedVersion
format dataset
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.21950/AN5SP2
url https://doi.org/10.21950/AN5SP2
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN//PID2021-122708OB-C31
info:eu-repo/grantAgreement/European Community/RYC2021-033921-I/
info:eu-repo/grantAgreement/University of Alcalá/PIUAH23%2FCC-046/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
CC-BY-NC-ND-4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv CC-BY-NC-ND-4.0
dc.format.none.fl_str_mv image/png
image/png
image/png
image/png
image/png
image/png
image/jpeg
image/png
image/png
image/png
image/png
image/png
image/png
image/png
text/plain
text/plain
text/plain
dc.publisher.none.fl_str_mv e-cienciaDatos
publisher.none.fl_str_mv e-cienciaDatos
dc.source.none.fl_str_mv reponame:e-cienciaDatos, Repositorio de Datos del Consorcio Madroño
instname:Consorcio Madroño
instname_str Consorcio Madroño
reponame_str e-cienciaDatos, Repositorio de Datos del Consorcio Madroño
collection e-cienciaDatos, Repositorio de Datos del Consorcio Madroño
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403168420397056
score 15,198674

Registos relacionados