Correlation of static aging effects on automotive catalysts
Bibliographic record
Abstract
This study identifies and analyzes the effect that aging time and temperature have on the CO light‐off activity of three‐way catalyst samples, aged in a static air (oxidizing) atmosphere. The bench aging time (BAT) equation proposed by the Environmental Protection Agency (EPA), which describes aging as dependent upon time at temperature, was used to calculate a range of oven aging times and temperatures based on a RAT‐A engine bench aging cycle. CO light‐off tests carried out on cores aged between 800 and 900 °C have shown that it is the aging temperature that has the greatest effect on catalyst deterioration for static aging testing, with aging time having little effect. These results were in contradiction to the BAT equation, an industry norm for the aging of catalysts. This demonstrates that static aging, whilst showing how temperature affects aging, gives little or no time effects. The results have shown that static aging is not representative of actual aging on a vehicle. Progressive aging conducted at a temperature of 1000 °C was shown to cause a decrease in catalyst activity as the aging time increased. However, even in these extreme conditions, static aging gave a slower rate of aging with time when compared to engine aging as defined by the BAT equation. Overall, static aging in air has been shown to produce a greater increase in aging due to temperature than predicted by the BAT equation, but less aging due to aging time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".