Hydration IndexA Better Parameter for Explaining Small Molecule Hydration in Inhibition of Ice Recrystallization
Bibliographic record
Abstract
Several simple mono- and disaccharides have been assessed for their ability to inhibit ice recrystallization. Two carbohydrates were found to be effective recrystallization inhibitors. D-galactose (1) was the best monosaccharide and D-melibiose (5) was the most active disaccharide. The ability of each carbohydrate to inhibit ice growth was correlated to its respective hydration number reported in the literature. A hydration number reflects the number of tightly bound water molecules to the carbohydrate and is a function of carbohydrate stereochemistry. It was discovered that using the absolute hydration number of a carbohydrate does not allow one to accurately predict its ability to inhibit ice recrystallization. Consequently, we have defined a hydration index in which the hydration number is divided by the molar volume of the carbohydrate. This new parameter not only takes into account the number of water molecules tightly bound to a carbohydrate but also the size or volume of a particular solute and ultimately the concentration of hydrated water molecules. The hydration index of both mono- and disaccharides correlates well with experimentally measured RI activity. C-Linked derivatives of the monosaccharides appear to have RI activity comparable to that of their O-linked saccharides but a more thorough investigation is required. The relationship between carbohydrate concentration and RI activity was shown to be noncolligative and a 0.022 M solution of D-galactose (1) and C-linked galactose derivative (10) inhibited recrystallization as well as a 3% DMSO solution. The carbohydrates examined in this study did not possess any thermal hysteresis activity (selective depression of freezing point relative to melting point) or dynamic ice shaping. As such, we propose that they are inhibiting recrystallization at the interface between bulk water and the quasi liquid layer (a semiordered interface between ice and bulk water) by disrupting the preordering of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".