Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models

Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxyb...

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Autores: Champa Bujaico, Elizabeth, Díez Pascual, Ana María|||0000-0001-7405-2354, García Díaz, María del Pilar|||0000-0002-5361-6947
Tipo de documento: artigo
Data de publicação:2023
País:España
Recursos:Universidad de Alcalá (UAH)
Repositório:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglês
OAI Identifier:oai:ebuah.uah.es:10017/64091
Acesso em linha:http://hdl.handle.net/10017/64091
https://dx.doi.org/10.3390/biom13081192
Access Level:Acceso aberto
Palavra-chave:Hybrid nanocomposites
Graphene oxide
Nanoclay
Green synthesis
Mechanical properties
Machine learning
Biomedical applications
Química
Chemistry
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spelling Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning modelsChampa Bujaico, ElizabethDíez Pascual, Ana María|||0000-0001-7405-2354García Díaz, María del Pilar|||0000-0002-5361-6947Hybrid nanocompositesGraphene oxideNanoclayGreen synthesisMechanical propertiesMachine learningBiomedical applicationsQuímicaChemistryPredicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R-2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.20232023-07-30journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/64091https://dx.doi.org/10.3390/biom13081192reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/640912026-06-18T11:13:07Z
dc.title.none.fl_str_mv Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
title Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
spellingShingle Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
Champa Bujaico, Elizabeth
Hybrid nanocomposites
Graphene oxide
Nanoclay
Green synthesis
Mechanical properties
Machine learning
Biomedical applications
Química
Chemistry
title_short Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
title_full Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
title_fullStr Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
title_full_unstemmed Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
title_sort Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclay bionanocomposites: Experimental results and theoretical predictions via machine learning models
dc.creator.none.fl_str_mv Champa Bujaico, Elizabeth
Díez Pascual, Ana María|||0000-0001-7405-2354
García Díaz, María del Pilar|||0000-0002-5361-6947
author Champa Bujaico, Elizabeth
author_facet Champa Bujaico, Elizabeth
Díez Pascual, Ana María|||0000-0001-7405-2354
García Díaz, María del Pilar|||0000-0002-5361-6947
author_role author
author2 Díez Pascual, Ana María|||0000-0001-7405-2354
García Díaz, María del Pilar|||0000-0002-5361-6947
author2_role author
author
dc.subject.none.fl_str_mv Hybrid nanocomposites
Graphene oxide
Nanoclay
Green synthesis
Mechanical properties
Machine learning
Biomedical applications
Química
Chemistry
topic Hybrid nanocomposites
Graphene oxide
Nanoclay
Green synthesis
Mechanical properties
Machine learning
Biomedical applications
Química
Chemistry
description Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R-2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-07-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/64091
https://dx.doi.org/10.3390/biom13081192
url http://hdl.handle.net/10017/64091
https://dx.doi.org/10.3390/biom13081192
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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