Semi-Automatic Image Analysis Tool for Cellulose Nanocrystal Particle Size Measurement from Atomic Force Microscopy Images

Cellulose nanocrystals (CNCs) are rod-like nanoparticles with that exhibit a unique combination of attractive characteristics for many applications: abundance, renewability, biocompatibility, desirable mechanical and chemical properties, and cheap production potential. Given these desirable properties, a broad spectrum of applications has been demonstrated in the literature for CNCs, from biomedical to composites, and from adhesives to sensors. Among the obstacles to the commercial utilization of CNCs is producing consistent quality, optimizing process parameters and reliable characterization protocols. Particle size and particle size distribution are particularly important when optimizing performance in any given application, for example as a rheology modifier in fluids or as a polymer reinforcement phase. Moreover, to optimize manufacturing processes, standardizing particle analysis is a crucial step. Currently the state-of-the-art approaches for characterizing CNC particle morphology are to manually measure individual particle dimensions in transmission electron microscopy or atomic force microscopy (AFM) images. This approach is not only time-consuming but also inconsistent because of bias or fatigue by human analyst. To address these characterization issues, a semi-automated image analysis framework based on MATLAB, called SMART, has been developed that segments AFM microscopic images into background and CNC particles, and classifies the identified CNC objects into isolated and agglomerated. This talk briefly introduces SMART, how it is applied to AFM image analysis by measuring the length and height of each CNC and reporting their distribution. This talk shows how the results are validated with the results of an inter-laboratory comparison study by Bushell et al., that assessed CNC size distribution using a reference CNC material that was characterized in four different laboratories. This talk critically compares the differences between the results obtained from SMART and from the traditional manual approaches. The results indicated that, while SMART’s measurements were in good agreement with the manual approach, its advantages are (a) significant reduction in analysis times of CNC characterization —from hours to minutes, and (b) employing a consistent approach as opposed to analyst subjectivity in manual measurements. This talk will discuss how SMART will facilitate CNC morphology characterization and how it may improve quality control assessment process and particle size optimization for a given application.