Evaluation of Butanol–Gasoline Blends in a Port Fuel-injection, Spark-Ignition Engine
Bibliographic record
Abstract
This paper assesses different butanol–gasoline blends used in a port fuel-injection, spark-ignition engine to quantify the influence of butanol addition on the emission of unburned hydrocarbons, carbon monoxide, and nitrogen oxide. Furthermore, in-cylinder pressure was measured to quantify combustion stability and to compare the ignition delay and fully developed turbulent combustion phases as given by 0%–10% and 10%–90% Mass Fraction Burned (MFB). The main findings are: 1) a 40% butanol/60% gasoline blend by volume (B40) minimizes HC emissions; 2) no significant change in NOx emissions were observed, with the exception of the 80% butanol/20% gasoline blend; 3) the addition of butanol improves combustion stability as measured by the COV of IMEP; 4) butanol added to gasoline reduces ignition delay (0%–10% MFB); and 5) the specific fuel consumption of B40 blend is within 10% of that of pure gasoline for stoichiometric mixture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".