Correlation Between Chain Architecture and Hydration Water Structure in Polysaccharides
Bibliographic record
Abstract
The physical properties of confined water can differ dramatically from those of bulk water. Hydration water associated with polysaccharides provides a particularly interesting example of confined water, because differences in polysaccharide structure provide different spatially confined environments for water sorption. We have used attenuated total reflection infrared (ATR-IR) spectroscopy to investigate the structure of hydration water in films of three different polysaccharides under controlled relative humidity (RH) conditions. We compare the results obtained for films of highly branched, dendrimer-like phytoglycogen nanoparticles to those obtained for two unbranched polysaccharides, hyaluronic acid (HA), and chitosan. We find similarities between the water structuring in the two linear polysaccharides and significant differences for phytoglycogen. In particular, the results suggest that the high degree of branching in phytoglycogen leads to a much more well-ordered water structure (low density, high connectivity network water), indicating the strong influence of chain architecture on the structuring of water. These measurements provide unique insight into the relationship between the structure and hydration of polysaccharides, which is important for understanding and exploiting these sustainable nanomaterials in a wide range of applications.
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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) | 0.001 | 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".