Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-o...

Autores
Cuss, C. W.; Benedetti, M. F.; Costamagna, Carla Antonella; Mesnard, Lucas; Tharaud, M.
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The development of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-ToF-MS) heralds a breakthrough in our ability to measure the multi-elemental composition of natural nanoparticles and colloids (NPs), and to characterize the dynamics, responses, and impacts of systems of natural NPs (NNPs). However, further developments and associated comparisons across studies and research groups are hindered by the lack of a consistent, reliable and comparable approach for detecting and differentiating NPs and NNPs. Self-organizing maps (SOM, aka Kohonen networks) are single-layer artificial neural networks that are widely used for pattern recognition and classification in the natural sciences and beyond. The SOM is a nonparametric statistical method which adapts to data structures and is robust to noise, outliers, and sparse data, making it especially suitable for peak detection and particle classification using raw spICP-ToF-MS time-series. This article provides a brief review of SOM and their outputs before demonstrating their ability to detect particles in spICP-ToF-MS time-series, and to characterize and compare NNPs. Additional considerations and research directions for the application of SOM to spICP-ToF-MS and particle data are then discussed. The raw data and algorithms used in this study are provided in the SI to facilitate the testing of SOM across research groups, and for comparing their performance with other methods.
Fil: Cuss, C. W.. Memorial University of Newfoundland; Canadá
Fil: Benedetti, M. F.. Universite de Paris; Francia
Fil: Costamagna, Carla Antonella. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Mesnard, Lucas. Université PSL; Francia
Fil: Tharaud, M.. Universite de Paris; Francia
Materia
NANOPARTICLES
MICROSCOPY
SCICP-TOF-MS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/281565

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spelling Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MSCuss, C. W.Benedetti, M. F.Costamagna, Carla AntonellaMesnard, LucasTharaud, M.NANOPARTICLESMICROSCOPYSCICP-TOF-MShttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The development of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-ToF-MS) heralds a breakthrough in our ability to measure the multi-elemental composition of natural nanoparticles and colloids (NPs), and to characterize the dynamics, responses, and impacts of systems of natural NPs (NNPs). However, further developments and associated comparisons across studies and research groups are hindered by the lack of a consistent, reliable and comparable approach for detecting and differentiating NPs and NNPs. Self-organizing maps (SOM, aka Kohonen networks) are single-layer artificial neural networks that are widely used for pattern recognition and classification in the natural sciences and beyond. The SOM is a nonparametric statistical method which adapts to data structures and is robust to noise, outliers, and sparse data, making it especially suitable for peak detection and particle classification using raw spICP-ToF-MS time-series. This article provides a brief review of SOM and their outputs before demonstrating their ability to detect particles in spICP-ToF-MS time-series, and to characterize and compare NNPs. Additional considerations and research directions for the application of SOM to spICP-ToF-MS and particle data are then discussed. The raw data and algorithms used in this study are provided in the SI to facilitate the testing of SOM across research groups, and for comparing their performance with other methods.Fil: Cuss, C. W.. Memorial University of Newfoundland; CanadáFil: Benedetti, M. F.. Universite de Paris; FranciaFil: Costamagna, Carla Antonella. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Mesnard, Lucas. Université PSL; FranciaFil: Tharaud, M.. Universite de Paris; FranciaRoyal Society of Chemistry2025-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/281565Cuss, C. W.; Benedetti, M. F.; Costamagna, Carla Antonella; Mesnard, Lucas; Tharaud, M.; Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS; Royal Society of Chemistry; Journal of Analytical Atomic Spectrometry; 40; 9; 8-2025; 2471-24860267-9477CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://pubs.rsc.org/en/content/articlelanding/2025/ja/d5ja00179jinfo:eu-repo/semantics/altIdentifier/doi/10.1039/D5JA00179Jinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-26T10:11:20Zoai:ri.conicet.gov.ar:11336/281565instacron: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:34982026-02-26 10:11:20.709CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
title Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
spellingShingle Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
Cuss, C. W.
NANOPARTICLES
MICROSCOPY
SCICP-TOF-MS
title_short Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
title_full Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
title_fullStr Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
title_full_unstemmed Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
title_sort Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS
dc.creator.none.fl_str_mv Cuss, C. W.
Benedetti, M. F.
Costamagna, Carla Antonella
Mesnard, Lucas
Tharaud, M.
author Cuss, C. W.
author_facet Cuss, C. W.
Benedetti, M. F.
Costamagna, Carla Antonella
Mesnard, Lucas
Tharaud, M.
author_role author
author2 Benedetti, M. F.
Costamagna, Carla Antonella
Mesnard, Lucas
Tharaud, M.
author2_role author
author
author
author
dc.subject.none.fl_str_mv NANOPARTICLES
MICROSCOPY
SCICP-TOF-MS
topic NANOPARTICLES
MICROSCOPY
SCICP-TOF-MS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The development of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-ToF-MS) heralds a breakthrough in our ability to measure the multi-elemental composition of natural nanoparticles and colloids (NPs), and to characterize the dynamics, responses, and impacts of systems of natural NPs (NNPs). However, further developments and associated comparisons across studies and research groups are hindered by the lack of a consistent, reliable and comparable approach for detecting and differentiating NPs and NNPs. Self-organizing maps (SOM, aka Kohonen networks) are single-layer artificial neural networks that are widely used for pattern recognition and classification in the natural sciences and beyond. The SOM is a nonparametric statistical method which adapts to data structures and is robust to noise, outliers, and sparse data, making it especially suitable for peak detection and particle classification using raw spICP-ToF-MS time-series. This article provides a brief review of SOM and their outputs before demonstrating their ability to detect particles in spICP-ToF-MS time-series, and to characterize and compare NNPs. Additional considerations and research directions for the application of SOM to spICP-ToF-MS and particle data are then discussed. The raw data and algorithms used in this study are provided in the SI to facilitate the testing of SOM across research groups, and for comparing their performance with other methods.
Fil: Cuss, C. W.. Memorial University of Newfoundland; Canadá
Fil: Benedetti, M. F.. Universite de Paris; Francia
Fil: Costamagna, Carla Antonella. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina
Fil: Mesnard, Lucas. Université PSL; Francia
Fil: Tharaud, M.. Universite de Paris; Francia
description The development of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-ToF-MS) heralds a breakthrough in our ability to measure the multi-elemental composition of natural nanoparticles and colloids (NPs), and to characterize the dynamics, responses, and impacts of systems of natural NPs (NNPs). However, further developments and associated comparisons across studies and research groups are hindered by the lack of a consistent, reliable and comparable approach for detecting and differentiating NPs and NNPs. Self-organizing maps (SOM, aka Kohonen networks) are single-layer artificial neural networks that are widely used for pattern recognition and classification in the natural sciences and beyond. The SOM is a nonparametric statistical method which adapts to data structures and is robust to noise, outliers, and sparse data, making it especially suitable for peak detection and particle classification using raw spICP-ToF-MS time-series. This article provides a brief review of SOM and their outputs before demonstrating their ability to detect particles in spICP-ToF-MS time-series, and to characterize and compare NNPs. Additional considerations and research directions for the application of SOM to spICP-ToF-MS and particle data are then discussed. The raw data and algorithms used in this study are provided in the SI to facilitate the testing of SOM across research groups, and for comparing their performance with other methods.
publishDate 2025
dc.date.none.fl_str_mv 2025-08
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/11336/281565
Cuss, C. W.; Benedetti, M. F.; Costamagna, Carla Antonella; Mesnard, Lucas; Tharaud, M.; Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS; Royal Society of Chemistry; Journal of Analytical Atomic Spectrometry; 40; 9; 8-2025; 2471-2486
0267-9477
CONICET Digital
CONICET
url http://hdl.handle.net/11336/281565
identifier_str_mv Cuss, C. W.; Benedetti, M. F.; Costamagna, Carla Antonella; Mesnard, Lucas; Tharaud, M.; Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS; Royal Society of Chemistry; Journal of Analytical Atomic Spectrometry; 40; 9; 8-2025; 2471-2486
0267-9477
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://pubs.rsc.org/en/content/articlelanding/2025/ja/d5ja00179j
info:eu-repo/semantics/altIdentifier/doi/10.1039/D5JA00179J
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Royal Society of Chemistry
publisher.none.fl_str_mv Royal Society of Chemistry
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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