Formation and short-range order of two-dimensional<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mi>Cu</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi>Pd</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>monolayer surface alloys on<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Ru</mml:mi><mml:mo>(</mml:mo><mml:mn>0001</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math>
Bibliographic record
Abstract
The overgrowth of Pd islands on $\mathrm{Ru}(0001)$ by Cu, the formation of atomically dispersed, two-dimensional surface alloys upon annealing to $780\phantom{\rule{0.3em}{0ex}}\mathrm{K}$, and the resulting distribution of surface atoms in these alloy films have been studied by ultrahigh vacuum-scanning tunneling microscopy (UHV-STM). The surface morphology of the resulting films and the atomic distribution within the surface alloys are governed by a favorization of $\mathrm{Cu}\text{\ensuremath{-}}\mathrm{Pd}$ neighborhoods over $\mathrm{Cu}\text{\ensuremath{-}}\mathrm{Cu}$ or $\mathrm{Pd}\text{\ensuremath{-}}\mathrm{Pd}$ pairs, as is already known from ordered and disordered ${\mathrm{Cu}}_{x}{\mathrm{Pd}}_{1\ensuremath{-}x}$ bulk alloys. Based on atomically resolved STM images with chemical contrast, which allow us to distinguish Cu and Pd surface atoms, the short-range order in the atomic distribution and the availability of specific adsorption ensembles are statistically evaluated. The results provide a basis for the microscopic understanding of $\mathrm{CO}$ adsorption data that have been previously published for the same surfaces [T. Hager, H. Rauscher, and R. J. Behm, Surf. Sci. 588, 181 (2004)]. Similarities and differences between two-dimensional surface alloys and three-dimensional bulk alloys of ${\mathrm{Cu}}_{x}{\mathrm{Pd}}_{1\ensuremath{-}x}$, focusing on the extent of order and on the role of the $\mathrm{Ru}(0001)$ substrate, are also discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".