Determination of molar mass distributions of highly oxidized dialdehyde cellulose by size exclusion chromatography and asymmetric flow field-flow fractionation
Source abstract
Abstract
Dialdehyde cellulose (DAC) has gained increasing interest because its (masked) aldehyde functionalities promise a rich follow-up chemistry and further conversion into different derivatives with varying properties. So far, the question of degradation upon DAC production and the resulting molar mass changes have remained unanswered since the problem of molar mass determination of DACs had not been solved. In this study, we present a comparison of two methods based on aqueous eluants, size exclusion chromatography and asymmetric flow field-flow fractionation (AsFlFFF), to determine the molar mass distribution of highly oxidized and thus water-soluble DACs, and to elucidate the impact of oxidation on the chain length of different cellulose substrates. Molar masses were determined by multi-angle laser light scattering in the case of both separation systems. The measurements were complemented by dynamic light scattering. AsFlFFF demonstrated an overall better separation efficiency, but at the expense of increased time consumption for method development. The results of molar mass analysis strongly depended on the eluant. Hemiacetal crosslinks are stable in near-neutral, low ionic strength eluants, while they have a tendency to be cleaved in acidic eluants or in solvents of higher ionic strength. By comparison of different dissolution strategies the optimal conditions for DAC analysis are proposed for both separation techniques. It is concluded that the extent of degradation upon dissolution can be significantly minimized by controlling the dissolution parameters, but degradation cannot be avoided completely.
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Research context
OpenAlex topics: Advanced Cellulose Research Studies · Extraction and Separation Processes · Innovative Microfluidic and Catalytic Techniques Innovation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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