The Impact of Isobutanol and Ethanol on Gasoline Fuel Properties and Black Carbon Emissions from Two Light-Duty Gasoline Vehicles
Bibliographic record
Abstract
This study reported black carbon (BC) mass and solid particle number emissions from a gasoline direct injection (GDI) vehicle and a port fuel injection (PFI) vehicle on splash blended E10 and iB16 fuels over the FTP-75 and US06 drive cycles at standard and cold ambient temperatures. For the FTP-75 drive cycle, the GDI vehicle had lower solid particle number and BC mass emissions from E10 (5.1×1012 particles/mile; 4.2 mg/mile) and iB16 (5.2×1012 particles/mile; 3.9 mg/mile) compared to E0 (7.2×1012 particles/mile; 7.0 mg/mi). Most of the reductions were attributed to the statistically significant reductions during the phases 1 and 2 of the FTP-75 drive cycle. iB16 was also observed to have statistically significant reduction on BC emissions when compared to E0 at cold ambient temperature but E10 did not show such BC reduction. For the PFI vehicle, most of the solid particle number and BC mass emissions were emitted primarily during phase 1 of the FTP-75 drive cycle. In general, solid particle number and BC mass emissions from the warmed PFI vehicle were low (0.5-1.3×1012 particles/mile; 0.2-0.8 mg/mi) and most of the fuel effects were not statistically significant. However, iB16 fuel was consistently observed to increase both solid particle number and BC mass emissions during phase 1 of the FTP-75 drive cycle while no statistically different solid particle number and BC mass emissions were observed for the E10 fuel.
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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) | 0.001 | 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".