Unusual polysaccharide rheology of aqueous dispersions of soft phytoglycogen nanoparticles
Bibliographic record
Abstract
Phytoglycogen is a natural polysaccharide produced in the form of dense, 35 nm diameter nanoparticles by some varieties of plants such as sweet corn. The highly-branched, dendrimeric structure of phytoglycogen leads to interesting and useful properties such as softness and deformability of the particles, and a strong interaction with water. These properties make the particles ideal for use as unique additives in personal care, nutrition and biomedical formulations. In the present study, we describe rheology measurements of aqueous dispersions of phytoglycogen nanoparticles. The viscosity of the dispersions remained Newtonian up to large concentrations (∼20% w/w). For higher concentrations, the zero-shear viscosity increased dramatically, reaching values that exceeded that of the water solvent by six orders of magnitude at a concentration of 30% w/w and were well described by the Vogel-Fulcher-Tammann relation of glassy dynamics. The very large values of the zero-shear viscosity are coupled with significant deformation of the soft nanoparticles. We quantified the softness of the particles by performing osmotic pressure measurements on concentrated dispersions, obtaining a value of 15 kPa for the compressional modulus. For the most concentrated samples, we observed flow at stresses less than the apparent yield stress value determined by fitting the high strain rate data to the Herschel-Bulkley model. This behavior, similar to that of star polymer glasses, suggests the possibility of a hairy colloid particle geometry. Remarkably, phytoglycogen nanoparticles dispersed in water provide a very simple experimental realization of glass-forming dispersions of soft colloidal particles that can be used to validate theoretical models in detail.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".