Thermal and Chemical Deactivation of Three-Way Catalysts: Comparison of Road-, Fuel-Cut and SAI- Aged Catalysts
Bibliographic record
Abstract
The objective of this study was to investigate which of the artificial aging cycles available in the automotive industry that causes major deactivation of three-way catalysts (TWCs) and can be used to obtain an aged catalyst similar to the road aged converter (160 000km). Standard bench cycle (SBC) aging with secondary air injection (SAI) covered aging with various mass flows - a flow from three cylinders into one catalyst system and a flow from three cylinders into two parallel connected catalysts. For rapid catalyst bench aging, secondary air injection is a very efficient tool to create exotherms. Furthermore, the effect on catalytic activity of SAI aging with poisons from oil and fuel dopants (P, Ca, Zn) was investigated. The catalysts were thoroughly characterized in light-off and oxygen storage capacity measurements, emission conversion as a function of lambda and load variation was determined. These catalysts were then compared to three-way catalysts aged on an engine bench comparing two fuel-cut (FC) strategies, i.e. fuel-cut retardation (FCR) and fuel-cut acceleration (FCA) [1]. For 40-hour aged samples, SAI aged had the highest total deactivation of the catalytic system. The fuel-cut aging also showed significant deactivation but not to the same extent as the SAI aging cycle. This result can be explained by the different pattern of exotherms and the magnitude of the temperature excursions. The total temperature rise in fuel-cut aging cycles was lower than for SAI aged and which resulted in less sintering of the catalytic washcoat. For the fuel-cut ageing systems the exotherms were growing with the ageing time. This was verified by resulting light-off and oxygen storage capacity (OSC) 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".