Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology

Ammonium acetate is employed in order to develop a novel HPLC-ICP-MS arsenic speciation methodology applicable to six arsenic species, i.e, AC, AB, AsIII, AsV, DMA and MMA. The most predominant species in the toxicological field are covered in a 30-min chromatogram with reproducible and repeatabilit...

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Detalles Bibliográficos
Autores: Rodríguez, P. F., López Colón, José Luis, Mendoza, J. L. de, Martín Aranda, Rosa María
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12766
Acceso en línea:https://hdl.handle.net/20.500.14468/12766
Access Level:acceso cerrado
Palabra clave:Urinary
Ammonium Acetate
Arsenic
Speciation
HPLC-ICP-MS
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spelling Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodologyRodríguez, P. F.López Colón, José LuisMendoza, J. L. deMartín Aranda, Rosa MaríaUrinaryAmmonium AcetateArsenicSpeciationHPLC-ICP-MSAmmonium acetate is employed in order to develop a novel HPLC-ICP-MS arsenic speciation methodology applicable to six arsenic species, i.e, AC, AB, AsIII, AsV, DMA and MMA. The most predominant species in the toxicological field are covered in a 30-min chromatogram with reproducible and repeatability peak area ratio. Moreover, typical problems from traditional methods are sorted out by using a robust, high-selective and 75ArCl+ interference-free methodology. Chromatographic and detector optimization ensures low LOQs for each species with acceptable precision and accuracy values obtained using four urinary arsenic speciation PTS enabling to be useful for sub ng mL−1 arsenic exposure assessments.Elseviere-Spacio UNED20242024-05-2020212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12766reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/closedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.esinfo:eu-repo/semantics/openAccessoai:e-spacio.uned.es:20.500.14468/127662026-06-06T12:38:31Z
dc.title.none.fl_str_mv Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
title Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
spellingShingle Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
Rodríguez, P. F.
Urinary
Ammonium Acetate
Arsenic
Speciation
HPLC-ICP-MS
title_short Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
title_full Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
title_fullStr Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
title_full_unstemmed Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
title_sort Ammonium acetate as a novel buffer for highly selective robust urinary HPLC-ICP-MS arsenic speciation methodology
dc.creator.none.fl_str_mv Rodríguez, P. F.
López Colón, José Luis
Mendoza, J. L. de
Martín Aranda, Rosa María
author Rodríguez, P. F.
author_facet Rodríguez, P. F.
López Colón, José Luis
Mendoza, J. L. de
Martín Aranda, Rosa María
author_role author
author2 López Colón, José Luis
Mendoza, J. L. de
Martín Aranda, Rosa María
author2_role author
author
author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Urinary
Ammonium Acetate
Arsenic
Speciation
HPLC-ICP-MS
topic Urinary
Ammonium Acetate
Arsenic
Speciation
HPLC-ICP-MS
description Ammonium acetate is employed in order to develop a novel HPLC-ICP-MS arsenic speciation methodology applicable to six arsenic species, i.e, AC, AB, AsIII, AsV, DMA and MMA. The most predominant species in the toxicological field are covered in a 30-min chromatogram with reproducible and repeatability peak area ratio. Moreover, typical problems from traditional methods are sorted out by using a robust, high-selective and 75ArCl+ interference-free methodology. Chromatographic and detector optimization ensures low LOQs for each species with acceptable precision and accuracy values obtained using four urinary arsenic speciation PTS enabling to be useful for sub ng mL−1 arsenic exposure assessments.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12766
url https://hdl.handle.net/20.500.14468/12766
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
info:eu-repo/semantics/closedAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
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
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
eu_rights_str_mv closedAccess
openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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