Machine learning toward high-performance electrochemical sensors

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorBrazilian Center for Research in Energy and Materials-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorFederal University of ABC-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGiordano, Gabriela F.-
Autor(es): dc.creatorFerreira, Larissa F.-
Autor(es): dc.creatorBezerra, Ítalo R. S.-
Autor(es): dc.creatorBarbosa, Júlia A.-
Autor(es): dc.creatorCosta, Juliana N. Y.-
Autor(es): dc.creatorPimentel, Gabriel J. C.-
Autor(es): dc.creatorLima, Renato S.-
Data de aceite: dc.date.accessioned2025-08-21T19:09:03Z-
Data de disponibilização: dc.date.available2025-08-21T19:09:03Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s00216-023-04514-z-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246639-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246639-
Descrição: dc.descriptionThe so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression models. Particularly, the use of supervised ML models trained on large data sets produced by electrical and electrochemical bio/sensors has emerged as an impacting trend in the literature by allowing accurate analyses even in the presence of usual issues such as electrode fouling, poor signal-to-noise ratio, chemical interferences, and matrix effects. In this trend article, apart from an outlook for the coming years, we present examples from the literature that demonstrate how helpful ML algorithms can be for dispensing the adoption of experimental methods to address the aforesaid interfering issues, ultimately contributing to translate testing technologies into on-site, practical, and daily applications. Graphical Abstract: [Figure not available: see fulltext.].-
Descrição: dc.descriptionBrazilian Nanotechnology National Laboratory Brazilian Center for Research in Energy and Materials, São Paulo-
Descrição: dc.descriptionInstitute of Chemistry University of Campinas, São Paulo-
Descrição: dc.descriptionCenter for Natural and Human Sciences Federal University of ABC, São Paulo-
Descrição: dc.descriptionSão Carlos Institute of Chemistry University of São Paulo, São Paulo-
Descrição: dc.descriptionSchool of Sciences São Paulo State University, São Paulo-
Descrição: dc.descriptionSchool of Sciences São Paulo State University, São Paulo-
Idioma: dc.languageen-
Relação: dc.relationAnalytical and Bioanalytical Chemistry-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAccuracy-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectData treatment-
Palavras-chave: dc.subjectRegression-
Título: dc.titleMachine learning toward high-performance electrochemical sensors-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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