Characterization of Size and Aggregation for CNC Dispersions Separated by Asymmetrical-flow Field-flow Fractionation
Jeremie Parot, Arnab K. Mukherjee, Martin Couillard, Maohui Chen, Shan Zou, Vincent A. Hackley, Linda Johnston
Cellulose nanocrystals derived from various types of cellulose biomass have significant potential for applications that take advantage of their availability from renewable natural resources and their high mechanical strength, biocompatibility and ease of modification. However, their high polydispersity and irregular rod-like shape present challenges for the quantitative dimensional determinations that are required for quality control of CNC production processes. Here we have fractionated a CNC certified reference material using a previously reported asymmetrical-flow field-flow fractionation (AF4) method and characterized selected fractions by atomic force microscopy and transmission electron microscopy. This work was aimed at addressing discrepancies in length between fractionated and unfractionated CNC and obtaining less polydisperse samples with fewer aggregates to facilitate microscopy dimensional measurements. The results demonstrate that early fractions obtained from an analytical scale AF4 separation contain predominantly individual CNCs. The number of laterally aggregated "dimers" and clusters containing 3 or more particles increases with increasing fraction number. Size analysis of individual particles by AFM for the early fractions demonstrates that the measured CNC length increases with increasing fraction number, in good agreement with the rod length calculated from the AF4 multi-angle light scattering data. The ability to minimize aggregation and polydispersity for CNC samples has important implications for correlating data from different sizing methods.
, Mukherjee, A.
, Couillard, M.
, Chen, M.
, Zou, S.
, Hackley, V.
and Johnston, L.
Characterization of Size and Aggregation for CNC Dispersions Separated by Asymmetrical-flow Field-flow Fractionation, Cellulose, [online], https://doi.org/10.1007/s10570-019-02909-9(0123456, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928789
(Accessed October 2, 2022)