Quantitative Studies of the Coverage Effects on Microkinetic Simulations for NO Oxidation on Pt(111)
Bibliographic record
Abstract
To advance a reliable microkinetic modeling approach using density functional theory (DFT) energies is of great importance to bridging between experimental results and theoretical calculations, and the current major issue is the coverage effect. In this work, a full microkinetic modeling for NO oxidation using DFT energetics is developed. We show that the calculated turnover frequency (TOF) (0.22 s –1 ) agrees with the experimental one (∼0.2 s –1 ) very well, if the coverage effects are properly incorporated. It is found that to include the interactions of adsorbates, namely, (i) O and O, NO and NO (self-interaction), and (ii) O and NO (cross-interaction), is important to obtain accurate kinetic results. Equally important, the interactions between the adsorbates and the transition states of O–O bond breaking and O–NO coupling are also crucial for achieving precise kinetics. We demonstrate that a two-line model can be used to describe accurately both the self and cross adsorbate–adsorbate interactions as well as the coverage effects on the transition states of O 2 dissociation and O–NO coupling. The various approximations including Brønsted–Evans–Polanyi (BEP) relations are carefully examined, and the errors involved are quantified. Moreover, a one-line model is tested, which is a simplified approach but gives rise to a good agreement with experimental results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".