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    • SCIENTIFIC PRODUCTION
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    • Dpto. Física de la Materia Condensada, Cristalografía y Mineralogía
    • DEP32 - Artículos de revista
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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/61221

    Título
    Rheological method for determining the molecular weight of collagen gels by using a machine learning technique
    Autor
    Nuñez Carrero, Karina CarlaAutoridad UVA Orcid
    Velasco Merino, CristianAutoridad UVA
    Asensio Valentín, MaríaAutoridad UVA
    Guerrero, Julia
    Merino Senovilla, Juan CarlosAutoridad UVA
    Año del Documento
    2022
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Polymers, 2022, Vol. 14, Nº. 17, 3683
    Abstract
    This article presents, for the first time, the results of applying the rheological technique to measure the molecular weights (Mw) and their distributions (MwD) of highly hierarchical biomolecules, such as non-hydrolyzed collagen gels. Due to the high viscosity of the studied gels, the effect of the concentrations on the rheological tests was investigated. In addition, because these materials are highly sensitive to denaturation and degradation under mechanical stress and temperatures close to 40 °C, when frequency sweeps were applied, a mathematical adjustment of the data by machine learning techniques (artificial intelligence tools) was designed and implemented. Using the proposed method, collagen fibers of Mw close to 600 kDa were identified. To validate the proposed method, lower Mw species were obtained and characterized by both the proposed rheological method and traditional measurement techniques, such as chromatography and electrophoresis. The results of the tests confirmed the validity of the proposed method. It is a simple technique for obtaining more microstructural information on these biomolecules and, in turn, facilitating the design of new structural biomaterials with greater added value.
    Materias (normalizadas)
    Collagen
    Colágeno
    Polymers
    Polímeros y polimerización
    Rheology
    Reología
    Molecular weights
    Machine learning
    Aprendizaje automático
    Materias Unesco
    22 Física
    23 Química
    ISSN
    2073-4360
    Revisión por pares
    SI
    DOI
    10.3390/polym14173683
    Patrocinador
    Instituto para la Competitividad Empresarial de Castilla y León (ICE), PROYECTOS I + D CENTROS TECNOLÓGICOS - (project CCTT3/20/VA/0006)
    Universidad de Valladolid - Postdoctoral Contract CONVOCATORIA 2020 (K.C.N.C)
    Version del Editor
    https://www.mdpi.com/2073-4360/14/17/3683
    Propietario de los Derechos
    © 2022 The Authors
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/61221
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Collections
    • DEP32 - Artículos de revista [284]
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    Universidad de Valladolid

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