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.

Figure 1.

Representative TEM images from labs T3 (left), T6 (centre) and T9 (right). The arrows mark features that are discussed in the text.

Figure 2.

Log-log cumulative probability plots for CNC length and width for laboratory T1 with fits of normal (N), lognormal (logN), skew normal (sN), and 2-mixture normal (N+N) distributions. The sN distribution provides a better fit than the other three models for both data sets.

Figure 3.

Representative data sets showing TEM histograms for length (top row) and width (bottom row) and a skew normal fit for the particle size distribution. Data sets were selected to show the range of distribution widths and shapes.

Figure 4.

Skew normal probability densities describing the individual laboratory results (black lines) and the corresponding consensus distribution (orange line).

Figure 5.

Degree of overlap (overlap index; relative common area between the two distributions) between data sets for width and length distributions.