Evaluation of Uncoated Gasoline Particulate Filter Performance for US EPA MY27+ Particulate Mass Emissions Regulation
Bibliographic record
Abstract
The gasoline particulate filter (GPF) represents a practical solution for particulate emissions control in light-duty gasoline-fueled vehicles. It is also seen as an essential technology in North America to meet the upcoming US EPA tailpipe emission regulation, as proposed in the “Multi-pollutant Rule for Model Year 2027”. The goal of this study was to introduce advanced, uncoated GPF products and measure their particulate mass (PM) reduction performance within the existing US EPA FTP vehicle testing procedures, as detailed in Code of Federal Regulations (CFR) part 1066. Various state-of-the-art GPF products were characterized for their microstructure properties with lab-bench checks for pressure drop and filtration efficiency, then pre-conditioned with an EPA-recommended 1500 mile on-road break-in, and finally were tested on an AWD vehicle chassis-dyno emissions test cell at both 25°C and -7°C ambient conditions. A modern, T3B70, GTDI light-duty truck served as the test vehicle platform for this study. This report will show that advanced uncoated GPFs can repeatedly demonstrate high PM filtration efficiency, positioning them well as one solution to meet the US EPA proposed 0.5 mg/mile PM limit. 
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".