Bibliographic record
Abstract
Abstract Ion implantation is applied here to prevent metallic silver (Ag) thin films from dewetting on a sapphire substrate during annealing. In these experiments, silicon (Si) and indium (In) atoms are implanted into Ag thin films grown directly on sapphire, which are then annealed for different time periods to introduce film dewetting. It is observed that trace amounts of 1014 cm−2 dopants significantly retard the film grain growth, alter the film surface wetting features, and trivially influence the electrical and optical performance of the original film. A grain growth model with the presence of solute species is introduced here, combined with a thermodynamic simulation of film dewetting. It is found that doping ions introduce solute drag into Ag grains thereby significantly retarding the grain growth by generating a limiting grain size. The shrunken grains then alter the film surface energy distribution, transferring the most stable state from the dewetting phase to the wetting phase. The approach provides a novel strategy to suppress metallic thin films from dewetting with high stability, durability, and insignificant impact on the film performance without using an adhesion layer and also potentially expands the thermal processing window for metallic thin films.
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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".