Characterization of the redispersibility of cellulose nanocrystals by particle size analysis using dynamic light scattering, TAPPI Journal April 2019

Application: The authors are attempting to characterize the redispersibility of CNC powders by particle size analysis using dynamic light scattering (DLS). Understanding the feasibility of measuring the particle size distribu-tion of CNCs with DLS should provide to an easy method for online quality control of CNCs production.

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Characterization of the redispersibility of cellulose nanocr
ABSTRACT: Cellulose nanocrystals (CNCs), which are derived from the most abundant and inexhaustible natural polymer, cellulose, have received significant interest owing to their mechanical, optical, chemical, and rheological properties. In order to transport CNC products conveniently and efficiently, they are ideally dried and stored as pow-ders using freeze-drying or spray-drying technologies. The redispersibility of CNC powders is quite important for their end use; hence, a convenient method is required to characterize the redispersibility of CNC powders. In this paper, the possibility of characterizing the redispersibility of CNC powders by particle size analysis using dynamic light scattering (DLS) was investigated by comparing the results from transmission electron microscopy (TEM) and DLS. The particle size obtained with DLS approximately matched that obtained with TEM. Compared with TEM, DLS is a quick and convenient method to measure the particle size distribution of CNCs in water. Two kinds of dispersing methods, sonication and high-speed shearing, and two kinds of CNCs prepared by different methods, sulfuric acid hydrolysis and the TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl) oxidization method, were used to study the redis-persibility of CNCs. Sonication was more efficient than the high-speed shearing method for nanoscale dispersion of CNC powders in water. CNCs prepared by sulfuric acid hydrolysis could be more easily redispersed in water than those prepared by TEMPO oxidation.
Author: Guomin Wu, Qian LI, Can Jin, Zhenwu Kong, and Siqun Wang

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