WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS

The analysis of energy loss near edge structures in EELS is a powerful method for a precise characterization of elemental oxidation states and local atomic coordination with an outstanding lateral resolution, down to the atomic scale. Given the complexity and sizes of the EELS spectrum images datase...

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Autores: Blanco Portals, Javier, Torruella, Pau, Baiutti, Federico, Anelli, Simone, Torrell, Marc, Tarancón, Albert, Peiró Martínez, Francisca, Estradé Albiol, Sònia
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/218243
Acceso en línea:https://hdl.handle.net/2445/218243
Access Level:acceso abierto
Palabra clave:Espectroscòpia de pèrdua d'energia d'electrons
Oxidació
Microscòpia electrònica de transmissió
Electron energy loss spectroscopy
Oxidation
Transmission electron microscopy
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spelling WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLSBlanco Portals, JavierTorruella, PauBaiutti, FedericoAnelli, SimoneTorrell, MarcTarancón, AlbertPeiró Martínez, FranciscaEstradé Albiol, SòniaEspectroscòpia de pèrdua d'energia d'electronsOxidacióMicroscòpia electrònica de transmissióElectron energy loss spectroscopyOxidationTransmission electron microscopyThe analysis of energy loss near edge structures in EELS is a powerful method for a precise characterization of elemental oxidation states and local atomic coordination with an outstanding lateral resolution, down to the atomic scale. Given the complexity and sizes of the EELS spectrum images datasets acquired by the state-of-the-art instrumentation, methods with low convergence times are usually preferred for spectral unmixing in quantitative analysis, such as multiple linear least squares fittings. Nevertheless, non-linear least squares fitting may be a superior choice for analysis in some cases, as it eliminates the need of calibrated reference spectra and provides information for each of the individual components included in the fitted model. To avoid some of the problems that the non-linear least squares algorithms may suffer dealing with mixed-composition samples and, thus, a model comprised by a large number of individual curves we proposed the combination of clustering analysis for segmentation and non-linear least squares fitting for spectral analysis. Clustering analysis is capable of a fast classification of pixels in smaller subsets divided by their spectral characteristics, and thus increases the control over the model parameters in separated regions of the samples, classified by their specific compositions. Furthermore, along with this manuscript we provide access to a self-contained and expandable modular software solution called WhatEELS. It was specifically designed to facilitate the combined use of clustering and NLLS, and includes a set of tools for white-lines analysis and elemental quantification. We successfully demonstrated its capabilities with a control sample of mesoporous cerium oxide doped with praseodymium and gadolinium, which posed challenging case-study given its spectral characteristics.Elsevier B.V.2025202520222025info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion1 p.application/pdfhttps://hdl.handle.net/2445/218243Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1016/j.ultramic.2021.113403Ultramicroscopy, 2022, vol. 232, p. 113403https://doi.org/10.1016/j.ultramic.2021.113403cc-by-nc-nd (c) Elsevier B.V., 2022http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2182432026-05-29T05:05:01Z
dc.title.none.fl_str_mv WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
title WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
spellingShingle WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
Blanco Portals, Javier
Espectroscòpia de pèrdua d'energia d'electrons
Oxidació
Microscòpia electrònica de transmissió
Electron energy loss spectroscopy
Oxidation
Transmission electron microscopy
title_short WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
title_full WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
title_fullStr WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
title_full_unstemmed WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
title_sort WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS
dc.creator.none.fl_str_mv Blanco Portals, Javier
Torruella, Pau
Baiutti, Federico
Anelli, Simone
Torrell, Marc
Tarancón, Albert
Peiró Martínez, Francisca
Estradé Albiol, Sònia
author Blanco Portals, Javier
author_facet Blanco Portals, Javier
Torruella, Pau
Baiutti, Federico
Anelli, Simone
Torrell, Marc
Tarancón, Albert
Peiró Martínez, Francisca
Estradé Albiol, Sònia
author_role author
author2 Torruella, Pau
Baiutti, Federico
Anelli, Simone
Torrell, Marc
Tarancón, Albert
Peiró Martínez, Francisca
Estradé Albiol, Sònia
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Espectroscòpia de pèrdua d'energia d'electrons
Oxidació
Microscòpia electrònica de transmissió
Electron energy loss spectroscopy
Oxidation
Transmission electron microscopy
topic Espectroscòpia de pèrdua d'energia d'electrons
Oxidació
Microscòpia electrònica de transmissió
Electron energy loss spectroscopy
Oxidation
Transmission electron microscopy
description The analysis of energy loss near edge structures in EELS is a powerful method for a precise characterization of elemental oxidation states and local atomic coordination with an outstanding lateral resolution, down to the atomic scale. Given the complexity and sizes of the EELS spectrum images datasets acquired by the state-of-the-art instrumentation, methods with low convergence times are usually preferred for spectral unmixing in quantitative analysis, such as multiple linear least squares fittings. Nevertheless, non-linear least squares fitting may be a superior choice for analysis in some cases, as it eliminates the need of calibrated reference spectra and provides information for each of the individual components included in the fitted model. To avoid some of the problems that the non-linear least squares algorithms may suffer dealing with mixed-composition samples and, thus, a model comprised by a large number of individual curves we proposed the combination of clustering analysis for segmentation and non-linear least squares fitting for spectral analysis. Clustering analysis is capable of a fast classification of pixels in smaller subsets divided by their spectral characteristics, and thus increases the control over the model parameters in separated regions of the samples, classified by their specific compositions. Furthermore, along with this manuscript we provide access to a self-contained and expandable modular software solution called WhatEELS. It was specifically designed to facilitate the combined use of clustering and NLLS, and includes a set of tools for white-lines analysis and elemental quantification. We successfully demonstrated its capabilities with a control sample of mesoporous cerium oxide doped with praseodymium and gadolinium, which posed challenging case-study given its spectral characteristics.
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/218243
url https://hdl.handle.net/2445/218243
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1016/j.ultramic.2021.113403
Ultramicroscopy, 2022, vol. 232, p. 113403
https://doi.org/10.1016/j.ultramic.2021.113403
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier B.V., 2022
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier B.V., 2022
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1 p.
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Electrònica i Biomèdica)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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