Parametric Analysis of Catalytic Converter Plugging Caused by Manganese-Based Gasoline Additives
Bibliographic record
Abstract
A parametric analysis, based on engine dynamometer tests, was performed to evaluate the influence of exhaust gas temperature, catalyst cell density, exhaust system configuration and the presence of the manganese fuel additive methylcyclopentadienyl manganese tricarbonyl (MMT) in motor fuel on catalytic converter deposits and plugging. Analysis of catalytic converter deposits revealed they consisted mainly of trimanganese tetroxide (Mn3O4), with traces of Ca, P, and Zn. Deposits on catalysts from customer vehicles from Canada, where MMT was known to be in the majority of gasoline in the 1999-2005 timeframe, and from road test vehicles were virtually identical to the catalyst deposits from the engine dynamometer tests. The engine dynamometer tests were conducted at three different exhaust gas temperatures (600° C, 715° C and 805°C), using two different catalyst cell densities (400 and 600 cells per square inch), and five different angles of incidence of the exhaust gas to the converter inlet surface (30°, 45°, 60°, 75° and 90°). These studies demonstrated that each of those three parameters has a significant influence on catalyst plugging by MMT. Higher cell density and close-coupled catalyst placement are two of the key technologies utilized to meet more stringent exhaust emissions standards that have been, or are being, enacted in many countries. The results demonstrate that these key emission control technologies are more susceptible to plugging from MMT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".