Superheating of ice crystals in antifreeze protein solutions
Bibliographic record
Abstract
It has been argued that for antifreeze proteins (AFPs) to stop ice crystal growth, they must irreversibly bind to the ice surface. Surface-adsorbed AFPs should also prevent ice from melting, but to date this has been demonstrated only in a qualitative manner. Here we present the first quantitative measurements of superheating of ice in AFP solutions. Superheated ice crystals were stable for hours above their equilibrium melting point, and the maximum superheating obtained was 0.44 degrees C. When melting commenced in this superheated regime, rapid melting of the crystals from a point on the surface was observed. This increase in melting temperature was more appreciable for hyperactive AFPs compared to the AFPs with moderate antifreeze activity. For each of the AFP solutions that exhibited superheating, the enhancement of the melting temperature was far smaller than the depression of the freezing temperature. The present findings clearly show that AFPs adsorb to ice surfaces as part of their mechanism of action, and this absorption leads to protection of ice against melting as well as freezing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".