Catalyzed Gasoline Particulate Filter Performance from Fresh to Full Useful Life
Bibliographic record
Abstract
The gasoline particulate filter (GPF) represents a durable solution for particulate emissions control in light-duty gasoline-fueled vehicles. It is also seen as a viable technology in North America to meet the upcoming US EPA tailpipe emission regulation, the proposed “Multipollutant Rule for Model Year 2027”. The goal of this study was to track the evolution of tailpipe particulate emissions of a modern GTDI light duty vehicle under typical North American mileage accumulation; from a fresh state to 4000-mile, and finally to its full useful life of 150,000-miles. For this purpose, a production TWC + GPF after-treatment system was installed in place of the T3B85 TWC-only system. Chassis dyno emissions testing was performed at the pre-determined mileage points with on-road driving conducted for the necessary mileage accumulation. This report will show the outstanding filtration durability and enhanced particulate control and of the current GPF technology all the way to 150,000 miles for the typical North American application. The report will also demonstrate that a coated CC2 GPF solution is an acceptable substitution for a flow through TWC catalyst for gaseous emissions performance, using an older 1st generation GPF washcoat technology without system optimization efforts. Noting that the investigation is an extension of previously reported results from the GPF performance from 0 to 4000-miles which characterized the early life evolution of tailpipe particulate emissions, specifically mass (PM) and number (PN).
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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) | 0.004 | 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".