Reactivity Retention of Modern Converters for Stringent Emission Control
Bibliographic record
Abstract
The growing emphasis on environmental protection and sustainability has resulted in increasingly stringent emission regulations for automotive manufacturers, as demonstrated by the upcoming EURO 7 and 2027 EPA standards. Significant advancements in cleaner combustion and effective aftertreatment strategies have been made in recent decades to increase the engine efficiency while abiding by the emission limits. Among the exhaust aftertreatment strategies, three-way catalyst has remained the primary solution for stoichiometric burn engines due to its high conversion efficiency and ability to simultaneously allow both oxidative and reductive reactions in a single stage with spatial separation due to the oxygen storage capabilities of ceria. However, fuel and lubricant-borne sulfur and phosphorus compounds have been shown to have a significant long-term effect on the activity of three-way catalysts, particularly during the lean-rich transitions and oxygen storage processes. In the present study, the impact of sulfur contamination on the conversion efficiency and oxygen storage capacity of the three-way catalyst has been investigated on a heated flow reactor bench platform. The influence of sulfur accumulation on the water-gas shift reaction and activity of ceria has been studied. Additionally, the process of sulfur removal at high temperature (~700-750°C) has also been explored. Relevant engine-out exhaust conditions from the SI engine platform, including flow, temperature, and exhaust species (individually), were replicated on a heated aftertreatment flow bench during contamination and regeneration cycles. A comprehensive analysis of species before and after the catalyst sections was performed using Fourier-transformed infrared (FTIR) and mass spectrometers to study and quantify the conversion and formation of species including sulfur species (sulfur dioxide and hydrogen sulfide) and hydrogen, under different catalyst conditions. The conversion selectivity of sulfur species during regeneration is also investigated. The results show that sulfur contamination causes a substantial reduction in oxygen storage capacity. Effective sulfur removal required a combination of high temperature (~700°C or higher) and lean-rich cycling; absence of either condition resulted in incomplete desulfation and the selectivity towards sulfur dioxide and hydrogen sulfide was largely dependent on the reductant species used during high temperature desulfation.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".