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
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/281565
Ver los metadatos del registro completo
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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. |
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2025 |
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2025-08 |
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article |
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publishedVersion |
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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 |
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http://hdl.handle.net/11336/281565 |
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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 |
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eng |
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eng |
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openAccess |
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Royal Society of Chemistry |
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Royal Society of Chemistry |
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