Ageing Studies of Pt- and Pd-Based Catalysts for the Combustion of Lean Methane Mixtures
Bibliographic record
Abstract
This paper presents results obtained for the thermal and hydrothermal ageing of seven commercial precious metals-based catalysts for the combustion of methane. Experiments are performed in a large excess of oxygen representing lean conditions. Temperatures used are those typically found in lean burn compression ignition engines. The precious metals used were platinum, palladium and rhodium, present either singly or in combination. The most active catalyst contains a platinum and palladium mixture, with palladium being dominant. This catalyst was also the least affected by both thermal and hydrothermal ageing. The second most active catalyst contained only palladium, but this catalyst also demonstrated more susceptibility to ageing. The least active catalyst contained only platinum, although this catalyst was also the least affected by hydrothermal ageing. The addition of rhodium to either palladium or platinum–palladium catalysts caused a more rapid loss in activity at higher temperatures, although the loss in activity at lower temperatures was similar in magnitude to those catalysts without rhodium. In some cases, cycling the reactor temperature between high and low restored some activity to the catalyst. In all cases, the catalyst activity was observed to be lower in the presence of water, after both thermal and hydrothermal ageing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".