Sputtering of surfaces by ion irradiation: A comparison of molecular dynamics and binary collision approximation models to laboratory measurements
Bibliographic record
Abstract
We compare various sputtering simulation methods to experimental results in both the low energy (<1 keV) and high energy (≥1 keV) impact regimes for argon ions impacting a pure copper substrate at normal incidence. Our results indicate that for high energy impacts, both binary collision approximation (BCA) and molecular dynamics methods can be used to generate reasonable predictions for the yield and energy distribution of the sputtered atoms. We also find reasonable agreement between the theoretical and experimental results down to impact energies of 600 eV. However, at 200 eV impact energies, significant discrepancies appear between the experimental and theoretical ejecta energy distributions in the peak position, the width of the energy distribution, and the magnitude of the high energy tail. These discrepancies appear to arise from the experimental results being only for atoms sputtered normal to the substrate surface, whereas the theoretical results are integrated over all 2π solid angles above the surface. Using the BCA code SDTrimSP and limiting the results to only atoms emitted within ±15° of the surface normal brings theory and experiment into reasonable agreement. These results suggest that for low energy impacts, the energy distribution of sputtered atoms is highly dependent on the emission angle of the ejecta.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".