A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements

Autores
Clementi, Luis Alberto; Vega, Jorge Ruben; Gugliotta, Luis Marcelino; Orlande, Helciio R. B.
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A statistical Bayesian method is proposed for estimating the particle size distribution (PSD) of polymeric latexes from multiangle dynamic light scattering (MDLS) measurements. The procedure includes two main steps: 1) the calculation of the angle-dependent average diameters of the PSD from the MDLS autocorrelation measurements, and 2) the PSD estimation through a Bayesian method (that is solved with a Markov chain sampling strategy implemented in the form of a Metropolis-Hasting algorithm). First, the method was evaluated through two simulated examples that involved unimodal and bimodal PSDs of different shapes. Then, the method was employed for estimating two bimodal PSDs obtained by mixing two narrow polystyrene standards. For comparison, all examples were also solved by numerical inversion of the raw MDLS autocorrelation measurements through a classical constrained regularization technique. The proposed method appears as an effective and robust tool for characterizing unimodal or multimodal PSDs without requiring any a priori assumption on the number of modes or on their shapes. For unimodal PSDs exhibiting high asymmetries and for bimodal PSDs with modes of different particle concentration, the Bayesian method produced more accurate results than those obtained with classical regularization techniques.
Fil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; Argentina
Fil: Gugliotta, Luis Marcelino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina
Fil: Orlande, Helciio R. B.. Federal University of Rio de Janeiro; Brasil
Materia
Latex
Particle Size Distribution
Dynamic Light Scattering
Inverse Problem
Regularization Techniques
Bayesian Method
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/13350
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurementsClementi, Luis AlbertoVega, Jorge RubenGugliotta, Luis MarcelinoOrlande, Helciio R. B.LatexParticle Size DistributionDynamic Light ScatteringInverse ProblemRegularization TechniquesBayesian Methodhttps://purl.org/becyt/ford/2.5https://purl.org/becyt/ford/2A statistical Bayesian method is proposed for estimating the particle size distribution (PSD) of polymeric latexes from multiangle dynamic light scattering (MDLS) measurements. The procedure includes two main steps: 1) the calculation of the angle-dependent average diameters of the PSD from the MDLS autocorrelation measurements, and 2) the PSD estimation through a Bayesian method (that is solved with a Markov chain sampling strategy implemented in the form of a Metropolis-Hasting algorithm). First, the method was evaluated through two simulated examples that involved unimodal and bimodal PSDs of different shapes. Then, the method was employed for estimating two bimodal PSDs obtained by mixing two narrow polystyrene standards. For comparison, all examples were also solved by numerical inversion of the raw MDLS autocorrelation measurements through a classical constrained regularization technique. The proposed method appears as an effective and robust tool for characterizing unimodal or multimodal PSDs without requiring any a priori assumption on the number of modes or on their shapes. For unimodal PSDs exhibiting high asymmetries and for bimodal PSDs with modes of different particle concentration, the Bayesian method produced more accurate results than those obtained with classical regularization techniques.Fil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; ArgentinaFil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; ArgentinaFil: Gugliotta, Luis Marcelino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); ArgentinaFil: Orlande, Helciio R. B.. Federal University of Rio de Janeiro; BrasilElsevier Science2011-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/13350Clementi, Luis Alberto; Vega, Jorge Ruben; Gugliotta, Luis Marcelino; Orlande, Helciio R. B.; A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 107; 1; 5-2011; 165-1730169-7439enginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2011.03.003info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743911000530info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:05:08Zoai:ri.conicet.gov.ar:11336/13350instacron: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 10:05:08.541CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
title A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
spellingShingle A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
Clementi, Luis Alberto
Latex
Particle Size Distribution
Dynamic Light Scattering
Inverse Problem
Regularization Techniques
Bayesian Method
title_short A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
title_full A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
title_fullStr A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
title_full_unstemmed A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
title_sort A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements
dc.creator.none.fl_str_mv Clementi, Luis Alberto
Vega, Jorge Ruben
Gugliotta, Luis Marcelino
Orlande, Helciio R. B.
author Clementi, Luis Alberto
author_facet Clementi, Luis Alberto
Vega, Jorge Ruben
Gugliotta, Luis Marcelino
Orlande, Helciio R. B.
author_role author
author2 Vega, Jorge Ruben
Gugliotta, Luis Marcelino
Orlande, Helciio R. B.
author2_role author
author
author
dc.subject.none.fl_str_mv Latex
Particle Size Distribution
Dynamic Light Scattering
Inverse Problem
Regularization Techniques
Bayesian Method
topic Latex
Particle Size Distribution
Dynamic Light Scattering
Inverse Problem
Regularization Techniques
Bayesian Method
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.5
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A statistical Bayesian method is proposed for estimating the particle size distribution (PSD) of polymeric latexes from multiangle dynamic light scattering (MDLS) measurements. The procedure includes two main steps: 1) the calculation of the angle-dependent average diameters of the PSD from the MDLS autocorrelation measurements, and 2) the PSD estimation through a Bayesian method (that is solved with a Markov chain sampling strategy implemented in the form of a Metropolis-Hasting algorithm). First, the method was evaluated through two simulated examples that involved unimodal and bimodal PSDs of different shapes. Then, the method was employed for estimating two bimodal PSDs obtained by mixing two narrow polystyrene standards. For comparison, all examples were also solved by numerical inversion of the raw MDLS autocorrelation measurements through a classical constrained regularization technique. The proposed method appears as an effective and robust tool for characterizing unimodal or multimodal PSDs without requiring any a priori assumption on the number of modes or on their shapes. For unimodal PSDs exhibiting high asymmetries and for bimodal PSDs with modes of different particle concentration, the Bayesian method produced more accurate results than those obtained with classical regularization techniques.
Fil: Clementi, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; Argentina
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico Para la Industria Química (i); Argentina. Universidad Tecnologica Nacional; Argentina
Fil: Gugliotta, Luis Marcelino. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química (i); Argentina
Fil: Orlande, Helciio R. B.. Federal University of Rio de Janeiro; Brasil
description A statistical Bayesian method is proposed for estimating the particle size distribution (PSD) of polymeric latexes from multiangle dynamic light scattering (MDLS) measurements. The procedure includes two main steps: 1) the calculation of the angle-dependent average diameters of the PSD from the MDLS autocorrelation measurements, and 2) the PSD estimation through a Bayesian method (that is solved with a Markov chain sampling strategy implemented in the form of a Metropolis-Hasting algorithm). First, the method was evaluated through two simulated examples that involved unimodal and bimodal PSDs of different shapes. Then, the method was employed for estimating two bimodal PSDs obtained by mixing two narrow polystyrene standards. For comparison, all examples were also solved by numerical inversion of the raw MDLS autocorrelation measurements through a classical constrained regularization technique. The proposed method appears as an effective and robust tool for characterizing unimodal or multimodal PSDs without requiring any a priori assumption on the number of modes or on their shapes. For unimodal PSDs exhibiting high asymmetries and for bimodal PSDs with modes of different particle concentration, the Bayesian method produced more accurate results than those obtained with classical regularization techniques.
publishDate 2011
dc.date.none.fl_str_mv 2011-05
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/11336/13350
Clementi, Luis Alberto; Vega, Jorge Ruben; Gugliotta, Luis Marcelino; Orlande, Helciio R. B.; A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 107; 1; 5-2011; 165-173
0169-7439
url http://hdl.handle.net/11336/13350
identifier_str_mv Clementi, Luis Alberto; Vega, Jorge Ruben; Gugliotta, Luis Marcelino; Orlande, Helciio R. B.; A Bayesian inversion method for estimating the particle size distribution of latexes from multiangle dynamic light scattering measurements; Elsevier Science; Chemometrics And Intelligent Laboratory Systems; 107; 1; 5-2011; 165-173
0169-7439
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2011.03.003
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169743911000530
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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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