Correlating Cellulose Nanocrystal Particle Size and Surface Area.

Area of Science:

• Materials Science
• Nanotechnology
• Analytical Chemistry

Background:

• Cellulose nanocrystals (CNCs) are nanomaterials with challenging characterization parameters like size distribution and surface area.
• Standard methods like atomic force microscopy (AFM) and transmission electron microscopy (TEM) have limitations in accurately measuring CNC dimensions and surface properties.

Purpose of the Study:

• To compare microscopy techniques (AFM, TEM) with dynamic light scattering (DLS) for CNC size distribution analysis.
• To develop and validate a solid-state Nuclear Magnetic Resonance (NMR) method for estimating CNC surface area, overcoming limitations of traditional BET methods.

Main Methods:

• Size distribution analysis using AFM and TEM, accounting for tip-convolution effects and lateral association.
• Dynamic light scattering (DLS) for routine analysis and trend examination with varying sonication energy to detect aggregates.
• Magic-angle-spinning (MAS) solid-state NMR to quantify surface area based on C4 site resonance ratios.

Main Results:

• AFM and TEM show good agreement for CNC length, but TEM widths are larger due to associated CNCs.
• Microscopy methods face limitations in individual particle selection and potential size bias during sample deposition.
• Solid-state NMR provides a reliable surface area estimate, consistent with elementary cellulose crystallite lateral dimensions, and overcomes aggregation issues seen with BET methods.

Conclusions:

• Solid-state NMR is a viable and accurate method for characterizing the surface area of aggregated nanomaterials like CNCs.
• The study highlights the importance of selecting appropriate characterization techniques for nanomaterials based on their properties and potential limitations.