Ice nucleation by electric surface fields of varying range and geometry
Bibliographic record
Abstract
Molecular dynamics simulations are employed to show that electric field bands acting only over a portion of a surface can function as effective ice nuclei. Field bands of different geometry (rectangular, triangular, and semicircular cross sectional areas are considered) all nucleate ice, provided that the band is sufficiently large. Rectangular bands are very efficient if the width and thickness are ≳0.35 nm, and ≳0.15 nm, respectively, and the necessary dimensions are comparable for other geometries. From these simulations we also learn more about the ice nucleation and growth process. Careful analysis of different systems reveals that ice strongly prefers to grow at (111) planes of cubic ice. This agrees with an earlier theoretical deduction based on considerations of water-ice interfacial energies. We find that ice nucleated by field bands usually grows as a mixture of cubic and hexagonal ice, consistent with other simulations of ice growth, and with experiment. This contrasts with simulations carried out with nucleating fields that span the entire surface area, where cubic ice dominates, and hexagonal layers are very rarely observed. We argue that this discrepancy is a simulation artifact related to finite sample size and periodic boundary 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 | 0.000 |
| 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".