Ice Nucleation Efficiency of Hydroxylated Organic Surfaces Is Controlled by Their Structural Fluctuations and Mismatch to Ice
Bibliographic record
Abstract
Heterogeneous nucleation of ice induced by organic materials is of fundamental importance for climate, biology, and industry. Among organic ice-nucleating surfaces, monolayers of long chain alcohols are particularly effective, while monolayers of fatty acids are significantly less so. As these monolayers expose to water hydroxyl groups with an order that resembles the one in the basal plane of ice, it was proposed that lattice matching between ice and the surface controls their ice-nucleating efficiency. Organic monolayers are soft materials and display significant fluctuations. It has been conjectured that these fluctuations assist in the nucleation of ice. Here we use molecular dynamic simulations and laboratory experiments to investigate the relationship between the structure and fluctuations of hydroxylated organic surfaces and the temperature at which they nucleate ice. We find that these surfaces order interfacial water to form domains with ice-like order that are the birthplace of ice. Both mismatch and fluctuations decrease the size of the preordered domains and monotonously decrease the ice freezing temperature. The simulations indicate that fluctuations depress the freezing efficiency of monolayers of alcohols or acids to half the value predicted from lattice mismatch alone. The model captures the experimental trend in freezing efficiencies as a function of chain length and predicts that alcohols have higher freezing efficiency than acids of the same chain length. These trends are mostly controlled by the modulation of the structural mismatch to ice. We use classical nucleation theory to show that the freezing efficiencies of the monolayers are directly related to their free energy of binding to ice. This study provides a general framework to relate the equilibrium thermodynamics of ice binding to a surface and the nonequilibrium ice freezing temperature and suggests that these could be predicted from the structure of interfacial 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".