Particle Size Distributions for Cellulose Nanocrystals Measured by Transmission Electron Microscopy: An Interlaboratory Comparison

Abstract

Particle size is a key parameter that must be measured to ensure reproducible production of cellulose nanocrystals (CNCs) and to achieve reliable performance metrics for specific CNC applications. Nevertheless, size measurements for CNCs are challenging due to their broad size distribution, irregular rod-shaped particles, and propensity to aggregate and agglomerate. We report an interlaboratory comparison (ILC) that tests transmission electron microscopy (TEM) protocols for image acquisition and analysis. Samples of CNCs were prepared on TEM grids in a single laboratory, and detailed data acquisition and analysis protocols were provided to participants. CNCs were imaged and the size of individual particles was analyzed in 10 participating laboratories that represent a cross section of academic, industrial, and government laboratories with varying levels of experience with imaging CNCs. The data for each laboratory were fit to a skew normal distribution that accommodates the variability in central location and distribution width and asymmetries for the various datasets. Consensus values were obtained by modeling the variation between laboratories using a skew normal distribution. This approach gave consensus distributions with values for mean, standard deviation, and shape factor of 95.8, 38.2, and 6.3 nm for length and 7.7, 2.2, and 2.9 nm for width, respectively. Comparison of the degree of overlap between distributions for individual laboratories indicates that differences in imaging resolution contribute to the variation in measured widths. We conclude that the selection of individual CNCs for analysis and the variability in CNC agglomeration and staining are the main factors that lead to variations in measured length and width between laboratories.

Particle size distributions for cellulose nanocrystals measured by atomic force microscopy: an interlaboratory comparison

“…The more rigorous ILC protocol for identification of individual CNCs for analysis may also contribute to size differences. The second observation is that the AFM and TEM ILC data (Meija et al 2020) confirm the previous report of a TEM width that is approximately twice the AFM height (Jakubek et al 2018) for CNCD-1. The consensus distributions have means and distribution widths of 7.7 nm and 2.2 nm for TEM width and 3.4 nm and 1.2 nm for AFM height.…”

“…The consensus distributions for the fitted parameters (Fig. 5) were obtained using the approach implemented in the TEM study in which all results were pooled together, an approach that makes the fewest number of assumptions (Meija et al 2020). The statistical model is similar to the approach used by Montoro Bustos (Montoro Bustos et al 2015), but with the use of skew normal distributions, rather than mixtures of Gaussian distributions, to model the individual data sets.…”

“…The AFM results can be compared to the TEM ILC results published separately (Meija et al 2020). Both…”

“…The ILC used a National Research Council of Canada (NRC) reference material, CNCD-1 (http:// www.nrc.ca/crm), with samples prepared by the pilot laboratory in order to reduce the impact of sample preparation and focus specifically on instrument operating conditions and image analysis. TEM results have been published separately (Meija et al 2020); the combined AFM and TEM results will provide the prenormative data necessary for an ISO/TC 229 (Nanotechnologies) technical specification (TS 23,151) on microscopy particle size measurements for CNCs. CNC particle size measurements were provided by 10 participating laboratories.…”

Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks

“…This is often the case when several particles are touching or lying on top of each other, a problem that is regularly faced when imaging non-ideal samples. The nonapplicability of fully automated segmentation for non-ideal nanoobjects such as cellulose nanocrystals holds also true for evaluation in TEM 20 . In all these cases, the automated generation or simulation of realistic-looking SEM images together with their ground truth segmentation masks could help tremendously with providing the necessary large amounts of training data that are not readily available by any other means.…”