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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.222 | 0.007 |
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; both teacher heads agree on what is shown here.
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".