Impact of nucleation on step-meandering instabilities during step-flow growth on vicinal surfaces
Bibliographic record
Abstract
Step-meandering instabilities can manifest during step-flow growth on vicinal surfaces [Bales and Zangwill, Phys. Rev. B 41, 5500 (1990); Pierre-Louis, D'Orsogna, and Einstein, Phys. Rev. Lett. 82, 3661 (1999)]. A phase diagram based on the various growth regimes of a vicinal surface allows us to study the impact of nucleation on these meanders and to predict a meandering instability caused by the nucleation and the coalescence of both islands and steps. Using an accelerated kinetic Monte Carlo method, we find that the coalescence of islands with steps produces large protrusions and deep ripples and that the resulting meandering instability is reinforced by the growth of the islands at almost the same positions from one monolayer to the other. A coarsening phenomenon occurs for the instability wavelength until mounds appear, favored by a large Ehrlich-Schwoebel barrier. Such a meandering instability could be exploited for periodic self-assembly.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".