Black Carbon Emissions in Gasoline Exhaust and a Reduction Alternative with a Gasoline Particulate Filter
Bibliographic record
Abstract
Black carbon (BC) mass and solid particle number emissions were obtained from two pairs of gasoline direct injection (GDI) vehicles and port fuel injection (PFI) vehicles over the U.S. Federal Test Procedure 75 (FTP-75) and US06 Supplemental Federal Test Procedure (US06) drive cycles on gasoline and 10% by volume blended ethanol (E10). BC solid particles were emitted mostly during cold-start from all GDI and PFI vehicles. The reduction in ambient temperature had significant impacts on BC mass and solid particle number emissions, but larger impacts were observed on the PFI vehicles than the GDI vehicles. Over the FTP-75 phase 1 (cold-start) drive cycle, the BC mass emissions from the two GDI vehicles at 0 °F (-18 °C) varied from 57 to 143 mg/mi, which was higher than the emissions at 72 °F (22 °C; 12-29 mg/mi) by a factor of 5. For the two PFI vehicles, the BC mass emissions over the FTP-75 phase 1 drive cycle at 0 °F varied from 111 to 162 mg/mi, higher by a factor of 44-72 when compared to the BC emissions of 2-4 mg/mi at 72 °F. The use of a gasoline particulate filter (GPF) reduced BC emissions from the selected GDI vehicle by 73-88% at various ambient temperatures over the FTP-75 phase 1 drive cycle. The ambient temperature had less of an impact on particle emissions for a warmed-up engine. Over the US06 drive cycle, the GPF reduced BC mass emissions from the GDI vehicle by 59-80% at various temperatures. E10 had limited impact on BC emissions from the selected GDI and PFI vehicles during hot-starts. E10 was found to reduce BC emissions from the GDI vehicle by 15% at standard temperature and by 75% at 19 °F (-7 °C).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".