Structure sensitivity and cluster size convergence for formate adsorption on copper surfaces: A DFT cluster model study
Bibliographic record
Abstract
The structure sensitivity and cluster size convergence for formate adsorption on the Cu(100), Cu(110) and Cu(111) surfaces have been investigated systematically using density functional theory and the cluster model containing up to 40 Cu atoms. The copper core–valence correlation effect on the adsorbate–surface interaction is examined by using three different basis sets and effective core potentials. The calculated geometries and vibrational frequencies are in good agreement with experimental data even on the small clusters and are not surface sensitive. However, the adsorption energies show strong dependence on the surface structure and the cluster size. The adsorption energies are shown to converge very well for the large clusters, and the activity of the Cu planes for formate adsorption is in the order of Cu(110)>Cu(100)>Cu(111), the same as that observed experimentally for methanol synthesis. Regardless of the basis set, cluster size and surface structure, all results show an anionic formate adsorption species. The chemisorption mechanism and the local structure of formate on the three copper surfaces are essentially very similar. Some discussion about cluster modeling is presented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".