Simplification of gel point characterization of cellulose nano and microfiber suspensions

Nanocellulose is an emerging material that needs to be well characterized to control its performance during industrial applications. Gel point (Øg) is a convenient parameter commonly used to estimate the aspect ratio (AR) of cellulose nano/microfibers (CNFs/CMFs), providing critical information on t...

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Detalles Bibliográficos
Autores: Sánchez Salvador, José Luis, Monte Lara, María Concepción, Negro Álvarez, Carlos Manuel, Batchelor, Warren, Garnier, Gil, Blanco Suárez, María Ángeles
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/7215
Acceso en línea:https://hdl.handle.net/20.500.14352/7215
Access Level:acceso abierto
Palabra clave:66.0
Aspect ratio
Cellulose nanofibers
Characterization
CSAPS smoothing spline
Gel point
Sedimentation
Industria del papel
Ingeniería química
3312.13 Tecnología de la Madera
3303 Ingeniería y Tecnología Químicas
Descripción
Sumario:Nanocellulose is an emerging material that needs to be well characterized to control its performance during industrial applications. Gel point (Øg) is a convenient parameter commonly used to estimate the aspect ratio (AR) of cellulose nano/microfibers (CNFs/CMFs), providing critical information on the nanofiber network. However, its estimation requires many sedimentation experiments, tedious and time consuming. In this study, a simpler and faster technique is presented to estimate Øg, based on one or two sedimentation experiments, reducing the experiments by a factor of at least 2.5. Here, this new methodology is successfully validated by using the Øg of different CNF/CMF hydrogels calculated with the traditional methodology, showing an error lower than 7%. The error in the estimation of the AR is lower than 3% in all cases. Furthermore, the two mathematical models currently used to estimate Øg, the smoothing spline and the quadratic fit, are compared and the mathematical assumptions improved.