Unraveling the Mechanism of Ice Nucleation by Mica (001) Surfaces
Bibliographic record
Abstract
Heterogeneous ice nucleation is an important process in atmospheric science, food preservation, and other areas of research. Muscovite mica is a commonly occurring mineral, and although its ice nucleating ability has been widely debated, recent experiments have established that some mica (001) surfaces efficiently nucleate ice. We employ molecular dynamics simulations to investigate ice nucleation by three variations of the mica (001) surface. These are bare surfaces devoid of counterions (B-mica), surfaces with ordered arrangements of K + counterions (K-mica), and protonated surfaces (H-mica). Our simulations show that B-mica and H-mica effectively nucleate ice, but K-mica does not. For B-mica and H-mica, the ice nucleation mechanism is unusual in that it does not occur via the basal or prism plane of I h . The mica (001) surfaces stabilize an ice bilayer resembling (but not identical to) the pyramidal (202̅1) plane of I h . This results in a mixed-phase ice nucleus consisting of hexagonal and cubic ice layers stacked in a particular order imposed by the surface. We discuss in detail the connections between surface composition, morphology, and ice nucleation. The influence of finite system size on ice nucleation is also investigated. Finally, we discuss our simulations in view of recent experimental results. Taken together, the experiments and simulations cast new light on ice nucleation by mica (001) surfaces.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".