Au-Induced Nanostructuring of Vicinal Si Surfaces
Bibliographic record
Abstract
We have investigated the Au-induced nanofaceting of vicinal silicon surfaces tilted from [111] towards [11 ]. Using samples miscut 3.8°, and 8°, from [111] respectively we have used scanning tunnelling microscopy (STM) to measure the surface morphology as a function of Au coverage (0.04 to 0.44 ML). As expected, Au adsorption produces dramatic changes in surface morphology on both samples. On the 3.8° sample we find that as little as 0.04 ML of Au is sufficient to remove the faceting present on the clean surface. As more Au is deposited 1-dimensional chain structures nucleate at step edges. These chain structures eventually grow to form (775)-Au facets. At 0.17 ML we observe a surface with Si(111)7 × 7 terraces and (775)-Au nanofacets. With more Au, the (111) terraces transform from a 7 × 7 to a 5 × 2 reconstruction and at 0.4 ML the sample consists of Si(111)5 × 2-Au terraces separated by (775)-Au facets. The morphology of the 8° sample also depends critically on Au coverage. Below 0.32 ML all 8° surfaces include (775)-Au nanofacets. Above 0.32 ML, the (775)-Au facet is no longer stable and Au is accommodated on the surface via the formation of higher angle facets with smaller chain spacing. In both samples, the persistence of the (775)-Au facet reinforces the idea that it represents a low energy facet on these Au modified vicinal 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 | 0.000 | 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".