Bibliographic record
Abstract
Silver iodide is one of the most effective ice nucleating agents known. Silver iodide particles induce heterogeneous ice nucleation at temperatures as warm as −3 °C. Consequently, silver iodide, particularly the hexagonal polymorph (β-AgI), has been the focus of recent research aimed at understanding the microscopic mechanism of ice nucleation. Molecular simulations have shown that the basal (0001) plane of β-AgI nucleates the basal plane of ice due to a good lattice match combined with favorable atomistic surface morphology. However, ice nucleation has not been previously observed for the primary prism (101̅0) face of β-AgI, despite its close lattice match to the primary prism plane of hexagonal ice (I h ). Here we report molecular dynamics simulations employing the TIP4P/Ice model at a lower temperature (230 K) than those considered in earlier simulations. Our simulations show spontaneous nucleation of I h by the primary prism face of β-AgI. Nucleation occurs via the primary prism face of I h . We show how the bilayer characteristic of the primary prism face of I h maps onto the surface morphology of β-AgI (101̅0). The primary prism face of β-AgI differs from the basal plane in that it has no electrical dipole perpendicular to the surface, which reduces the likelihood that an exposed β-AgI (101̅0) surface will undergo reconstruction. Thus, the primary prism plane might be a more viable possibility for ice nucleation by real β-AgI particles. Simulations were also performed for the secondary prism (112̅0) face of β-AgI, but ice nucleation was not observed on simulation time scales. Nevertheless, detailed geometric analysis shows that the atomistic morphology of this surface could provide a good match to the secondary prism plane of I h .
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".