Liquid-State NMR Analysis of Nanocelluloses
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abstract
Recent developments in ionic liquid electrolytes for cellulose or biomass dissolution has also allowed for high-resolution 1H and 13C NMR on very high molecular weight cellulose. This permits the development of advanced liquid-state quantitative NMR methods for characterization of unsubstituted and low degree of substitution celluloses, for example, surface-modified nanocelluloses, which are insoluble in all molecular solvents. As such, we present the use of the tetrabutylphosphonium acetate ([P4444][OAc]):DMSO- d6 electrolyte in the 1D and 2D NMR characterization of poly(methyl methacrylate) (PMMA)-grafted cellulose nanocrystals (CNCs). PMMA- g-CNCs was chosen as a difficult model to study, to illustrate the potential of the technique. The chemical shift range of [P4444][OAc] is completely upfield of the cellulose backbone signals, avoiding signal overlap. In addition, application of diffusion-editing for 1H and HSQC was shown to be effective in the discrimination between PMMA polymer graft resonances and those from low molecular weight components arising from the solvent system. The bulk ratio of methyl methacrylate monomer to anhydroglucose unit was determined using a combination of HSQC and quantitative 13C NMR. After detachment and recovery of the PMMA grafts, through methanolysis, DOSY NMR was used to determine the average self-diffusion coefficient and, hence, molecular weight of the grafts compared to self-diffusion coefficients for PMMA GPC standards. This finally led to a calculation of both graft length and graft density using liquid-state NMR techniques. In addition, it was possible to discriminate between triads and tetrads, associated with PMMA tacticity, of the PMMA still attached to the CNCs (before methanolysis). CNC reducing end and sulfate half ester resonances, from sulfuric acid hydrolysis, were also assignable. Furthermore, other biopolymers, such as hemicelluloses and proteins (silk and wool), were found to be soluble in the electrolyte media, allowing for wider application of this method beyond just cellulose analytics.
