Structure and Hydration of Highly-Branched, Monodisperse Phytoglycogen Nanoparticles
Bibliographic record
Abstract
Phytoglycogen is a naturally occurring polysaccharide nanoparticle made up of extensively branched glucose monomers. It has a number of unusual and advantageous properties, such as high water retention, low viscosity, and high stability in water, which make this biomaterial a promising candidate for a wide variety of applications. In this study, we have characterized the structure and hydration of aqueous dispersions of phytoglycogen nanoparticles using neutron scattering. Small angle neutron scattering results suggest that the phytoglycogen nanoparticles behave similar to hard sphere colloids and are hydrated by a large number of water molecules (each nanoparticle contains between 250% and 285% of its mass in water). This suggests that phytoglycogen is an ideal sample in which to study the dynamics of hydration water. To this end, we used quasielastic neutron scattering (QENS) to provide an independent and consistent measure of the hydration number, and to estimate the retardation factor (or degree of water slow-down) for hydration water translational motions. These data demonstrate a length-scale dependence in the measured retardation factors that clarifies the origin of discrepancies between retardation factor values reported for hydration water using different experimental techniques. The present approach can be generalized to other systems containing nanoconfined water.
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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".