The Influence of pH on Ice Nucleation by α-alumina 
Bibliographic record
Abstract
Heterogeneous ice nucleation refers to ice nucleation initiated by an ice nucleating particle (INP). Important INPs include mineral dust and biological particles. Cloud conditions, such as pH, significantly affect ice nucleation. Clouds are generally acidic but can have a range of pH values, depending on their compositions. Studies have shown that α -alumina is an efficient INP in both laboratory experiments and Molecular Dynamics (MD) simulations. The (0001) plane of α-alumina is covered by hydroxyl groups in aqueous solutions, therefore, the surface is expected to undergo dual protonation (acidic conditions) and deprotonation (basic conditions). We investigate the effect of pH on the ice-nucleating efficiency of the α-alumina (0001) plane in MD simulation. Multiple surface proton coverages are considered, and we relate the surface proton coverage to pH through pKa values reported in the literatures. Among all possible surface proton coverages, the mono-protonated surface, which dominates under neutral condition, appears to be most efficient in nucleating the basal plane ice. For dual-protonated and deprotonated surfaces, the ice bilayer above the surface becomes less ice-like, leading to less efficient ice nucleation. Our MD results suggest that the (0001) plane of α-alumina is most efficient in nucleating ice under neutral condition, and less efficient under acidic and basic conditions.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".