Assessment of the Fuel Composition Impact on Black Carbon Mass, Particle Number Size Distributions, Solid Particle Number, Organic Materials, and Regulated Gaseous Emissions from a Light-Duty Gasoline Direct Injection Truck and Passenger Car
Bibliographic record
Abstract
The influence of the aromatic hydrocarbons in gasoline on the fuel distillation parameter, as well as the particle number (PN), black carbon (BC), and other regulated gaseous emissions from a passenger car (PC) and light-duty truck (LDT), was assessed by operating two vehicles fueled with U.S. Environmental Protection Agency Tier 3 certification gasoline and two gasoline test fuels over two standard drive cycles. The two gasoline test fuels represent a range of commercial motor gasoline, with one containing less naphthalenes and lower heavy fraction volatility (T80, T90, and final boiling point) than the other. Observations showed that various gasolines have minor impact on both vehicles on regulated gaseous emissions and fuel consumption. Particulate emissions from both vehicles showed similar trends with fuel type, with lower naphthalene containing gasoline produced lower PN and BC emissions. In addition, the effect of fuel on particle emissions varied with vehicle type, drive cycle, and power to weight ratio. Results also showed that lowering the naphthalenes in gasoline produces smaller sized particles. The real-time particle emission time series from both vehicles suggested that the composition and volatility of the gasoline fuels are sensitive parameters in influencing particulate matter emissions. These results could support one possible explanation of the large variations in emission factors reported in the literature when using different gasolines in the same type of vehicle and driving conditions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".