Catalyzed Diesel Particulate Filter Performance in A Light-Duty Vehicle
Bibliographic record
Abstract
Light-duty chassis dynamometer driving cycle tests were conducted on a Mercedes A170 diesel vehicle with various sulfur-level fuels and exhaust emission control systems. Triplicate runs of a modified light-duty federal test procedure (FTP), US06 cycle, and SCO3 cycle were conducted with each exhaust configuration and fuel. Ultra-low sulfur (3-ppm) diesel fuel was doped to 30- and 150-ppm sulfur so ppm sulfur so that all other fuel properties remained the same. The fuels used in these experiments met the specifications of the fuels from the DECSE (Diesel Emission Control Sulfur Effects) program. Although the Mercedes A170 vehicle is not available in the United States, its emissions in the as tested condition fell within the U.S. Tier 1 full useful life standards with the OEM catalysts installed. Tests with the OEM catalysts removed showed that the OEM catalysts reduced PM emissions from the engine-out condition by 30-40% but had negligible effects on NOx emissions. Fuel sulfur level had very little effect on the OEM catalyst performance. A prototype catalyzed diesel particulate filter (CDPF) mounted in an underfloor configuration reduced particulate matter emissions by more than 90% compared to the factory emissions control system. The results show that the CDPF did not promote any significant amounts of SO2-to-sulfate conversion during these light-duty drive cycles.
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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) | 0.002 | 0.001 |
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".