Cement classification and characterization using Non-Invasive techniques

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorInstituto de Química Rosario (CONICET-UNR)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.creatorRomero, Esteban-
Autor(es): dc.creatorFerreira, Dennis S.-
Autor(es): dc.creatorPereira, Fabiola M.V.-
Autor(es): dc.creatorOlivieri, Alejandro C.-
Autor(es): dc.creatorPereira-Filho, Edenir R.-
Autor(es): dc.creatorArancibia, Juan A.-
Data de aceite: dc.date.accessioned2025-08-21T19:46:34Z-
Data de disponibilização: dc.date.available2025-08-21T19:46:34Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.talanta.2024.127212-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303582-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303582-
Descrição: dc.descriptionCement is one of the most widely used materials in the global construction industry, serving as an adhesive and binder in projects that require strength and durability. Additionally, cement production indicates a country's development and economic activity, with global production reaching approximately 4 billion tons annually. It is a fine powder composed mainly of lime, silica, iron oxide, and alumina. Portland cement is the most common type, although a wide variety of types of cement differ in their chemical composition, providing them with specific properties for different applications. A set of fifty samples, consisting of eleven primary samples and thirty-nine blends formed by the combination of these eleven samples, was prepared. Additionally, twenty-four samples were randomly selected for error covariance calculation. Subsequently, two analytical techniques, laser-induced breakdown spectroscopy (LIBS) and energy dispersive X-ray fluorescence (ED-XRF) were applied to quantify aluminum (Al), calcium (Ca), iron (Fe), potassium (K), magnesium (Mg), sodium (Na), and sulfur (S). Afterward, the samples were analyzed via ICP OES after acid mineralization with 8 mL aqua regia in a digester block. Multivariate calibration strategies such as principal component regression (PCR), maximum likelihood principal component regression (MLPCR), partial least-squares regression (PLS), and error covariance penalized regression (ECPR) were employed. Finally, figures of merit were calculated to verify the most suitable models. The results revealed robust models with notable sensitivity, ranging from 0.3 to 329 signal a.u (% w w−1)−1), low limits of detection (LoD) within the range of 0.00–0.1 % w w−1, and remarkable accuracy ranging from 67.8 % to 140.3 %, particularly for Ca, Fe, Mg, and Na. This research takes an essential step in developing simple analytical methods with low waste generation and less environmental impact, thanks to using novel chemometric techniques to process the data.-
Descrição: dc.descriptionAgencia Nacional de Promoción Científica y Tecnológica-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionConsejo Nacional de Investigaciones Científicas y Técnicas-
Descrição: dc.descriptionUniversidad Nacional de Rosario-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartamento de Química Analítica Facultad de Ciencias Bioquímicas y Farmacéuticas Universidad Nacional de Rosario Instituto de Química Rosario (CONICET-UNR), Suipacha 531-
Descrição: dc.descriptionInstitute of Chemistry Paulista State University, São Paulo state-
Descrição: dc.descriptionGroup of Applied Instrumental Analysis Department of Chemistry Federal University of São Carlos, P.O. Box 676, São Paulo State-
Descrição: dc.descriptionInstitute of Chemistry Paulista State University, São Paulo state-
Descrição: dc.descriptionCNPq: 140867/2021-0-
Descrição: dc.descriptionFAPESP: 2016/17221-8-
Descrição: dc.descriptionFAPESP: 2019/01102–8-
Descrição: dc.descriptionFAPESP: 2019/24223-5-
Descrição: dc.descriptionFAPESP: 2021/10882-7-
Descrição: dc.descriptionFAPESP: 2022/02232-5-
Descrição: dc.descriptionCNPq: 302085/2022–0-
Descrição: dc.descriptionCNPq: 302719/2020-2-
Descrição: dc.descriptionCNPq: 307328/2019-8-
Idioma: dc.languageen-
Relação: dc.relationTalanta-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCement powder-
Palavras-chave: dc.subjectData fusion-
Palavras-chave: dc.subjectED-XRF-
Palavras-chave: dc.subjectFigures of merit-
Palavras-chave: dc.subjectLIBS-
Palavras-chave: dc.subjectMultivariate calibration-
Título: dc.titleCement classification and characterization using Non-Invasive techniques-
Tipo de arquivo: dc.typelivro digital-
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