Influence of surface vacancy defects on the carburisation of Fe 110 surface by carbon monoxide
Bibliographic record
Abstract
Adsorption and dissociation of gaseous carbon monoxide (CO) on metal surfaces is one of the most frequently occurring processes of carburisation, known as primary initiator of metal dusting corrosion. Among the various factors that can significantly influence the carburisation process are the intrinsic surface defects such as single surface vacancies occurring at high concentrations due to their low formation energy. Intuitively, adsorption and dissociation barriers of CO are expected to be lowered in the vicinity of a surface vacancy, due to the strong attractive interaction between the vacancy and the C atom. Here the adsorption energies and dissociation pathways of CO on clean and defective Fe 110 surface are explored by means of density functional theory. Interestingly, we find that the O adatom, resulting from the CO dissociation, is unstable in the electron-deficit neighbourhood of the vacancy due to its large electron affinity, and raises the barrier of the carburisation pathway. Still, a full comparative study between the clean surface and the vacancy-defected surface reveals that the complete process of carburisation, starting from adsorption to subsurface diffusion of C, is more favourable in the vicinity of a vacancy defect.
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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".