N2O selectivity in industrial NH3 oxidation on Pt-gauze is determined by interaction of local flow and surface chemistry: A simulation study using mechanistic kinetics
Bibliographic record
Abstract
Despite significant effort spent on the investigation of catalytic ammonia oxidation at the molecular scale, there is surprisingly little work that investigates the behavior of the established reaction mechanisms under industrial conditions in the presence of mass transfer limitations. This paper presents reactive flow simulations of ammonia oxidation on platinum gauzes under industrial operating conditions, combining a mechanistic description of the surface chemistry with the computation of the flow-, temperature and concentration fields around the platinum wires. Overall, the simulations yield temperature- and concentration fields, as well as integral N2O selectivity in line with industrial experience and (limited available) experimental data. In particular, the simulations predict the experimentally observed increase of the integral N2O selectivity with increasing flow velocity, decreasing wire diameter and wire-to-wire distance, and increased surface area due to surface reconstruction. The main result of the paper is that the local interaction of flow and surface chemistry leads to a variation in the local N2O selectivity across the gauze:The N2O and N2 selectivity is higher on the front side of a wire than on the rear side. A reduced N2O selectivity is observed where one wire is shadowed by another wire. Increased N2O selectivity is observed at stagnation points where upstream wires direct the flow so that it hits a downstream wire with higher velocity. These examples show that - through the flow directing effect of the upstream wires- the selectivity on an individual wire is influenced by the presence of other wires. This observation provides a mechanistic explanation for the industrial observation that optimized gauze geometries can lead to reduced N2O formation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".