Inhibition of Ice Growth and Recrystallization by Zirconium Acetate and Zirconium Acetate Hydroxide
Bibliographic record
Abstract
The control over ice crystal growth, melting, and shaping is important in a variety of fields, including cell and food preservation and ice templating for the production of composite materials. Control over ice growth remains a challenge in industry, and the demand for new cryoprotectants is high. Naturally occurring cryoprotectants, such as antifreeze proteins (AFPs), present one solution for modulating ice crystal growth; however, the production of AFPs is expensive and inefficient. These obstacles can be overcome by identifying synthetic substitutes with similar AFP properties. Zirconium acetate (ZRA) was recently found to induce the formation of hexagonal cavities in materials prepared by ice templating. Here, we continue this line of study and examine the effects of ZRA and a related compound, zirconium acetate hydroxide (ZRAH), on ice growth, shaping, and recrystallization. We found that the growth rate of ice crystals was significantly reduced in the presence of ZRA and ZRAH, and that solutions containing these compounds display a small degree of thermal hysteresis, depending on the solution pH. The compounds were found to inhibit recrystallization in a manner similar to that observed in the presence of AFPs. The favorable properties of ZRA and ZRAH suggest tremendous potential utility in industrial 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".