In-depth characterization of the aggregation state of cellulose nanocrystals through analysis of transmission electron microscopy images. (15th February 2021)

• Record Type: Journal Article
• Authors: Campano, Cristina; Lopez-Exposito, Patricio; Gonzalez-Aguilera, Laura; Blanco, Ángeles; Negro, Carlos
• Is Part Of: Carbohydrate polymers. Volume 254(2021)
• Publication Date: 2021-02-15
• Subjects: Cellulose nanocrystals -- Nanocellulose -- Transmission electron microscopy -- Aggregation state -- k-Means clustering -- Dispersion; Polysaccharides -- Periodicals; Polysaccharides -- Periodicals; Polysaccharides -- Périodiques; Electronic journals; 547.78
• DOI: 10.1016/j.carbpol.2020.117271
• Languages: English
• ISSNs: 0144-8617

Abstract

Graphical abstract: Highlights: New characterization of CNC aggregation by TEM and morphological classification. Models trained with large number of CNC aggregates of diverse morphology and source. It can detect application-critical high complexity, low compactness CNC aggregates. Models are persistent and reusable for characterization of other CNC aggregates. Approach and models usable as standard for CNC characterization with other methods. Abstract: Dispersion of cellulose nanocrystals (CNCs) is of utmost importance to guarantee their reliable application. Nevertheless, there is still no consensual method to characterize CNC aggregation. The hypothesis of this paper is that dispersion could be quantified through the classification of aggregates detected in transmission electron microscopy images. k-Means was used to classify image particulate elements of five CNC samples into groups according to their geometric features. Particles were classified into five groups according to their maximum Feret diameter, elongation, circularity and area. Two groups encompassed the most application-critical aggregates: one integrated aggregates of high complexity and low compactness while the other included elongated aggregates. In addition, the characterization of CNC dispersion after different levels of sonication was achieved by assessing the change in the number of elements belonging to each cluster after sonication. This approach could be used as a standard for the characterization of the aggregation state of CNCs.