<title>Large-scale computer simulations of metal/oxide interfaces with defects</title>
Bibliographic record
Abstract
Ab initio slab simulations have been performed for silver adhesion to the perfect and defective MgO(001) surfaces. For 1/4 Ag monolayer (ML) coverage of perfect substrate, we observe small silver adhesion energies over both O2- and Mg2+ ions on a regular MgO(001) substrate (0.23 and 0.22 eV per Ag atom, respectively), with negligible interfacial charge transfer towards metal atoms. For larger Ag coverages (beginning with 1/2 ML), silver adsorption over regular O2- ions is much more favorable. We demonstrate that point surface defects on a magnesia surface increase markedly the metal adhesion energy and cause a redistribution of the electron density across the interface. The results for electron (Fs° = O vacancy with two trapped electrons) and hole (Vs° = Mg vacancy with two holes trapped by nearest O2- ions) centers in the Ag atom adhesion at different surface coverages are analyzed. For Ag atoms positioned over the point surface defects, the substrate binding energies increase by more than an order of magnitude (to 7.6 and 12.7 eV, repsectively) compared to a regular interface and are associated with marked charge transfer (approximately 1 e towards a Ag atom over a Fs center and approximately 1.5 e towards the nearest O2- ions from a Ag atom over a Vs center). A comparison of these results with silver adhesion on α-Al2O3(0001) surfaces shows two similarities in the nature of the adhesion for Ag adsorption: (1) on the perfect MgO(001) and Al-terminated (stoichiometric) corundum (0001) substrates we observe physisorption with a weak atomic polarization, whereas (2) over a Vs center on the defective MgO and for O-terminated corundum, strong interfacial ionic bonding takes place.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".