Impact of Ethanol and Isobutanol Gasoline Blends on Emissions from a Closed-Loop Small Spark-Ignited Engine
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The focus of this study was the characterization and comparison of power-specific exhaust emission rates from a closed-loop small spark-ignited engine fuelled with ethanol and isobutanol gasoline blends.</div><div class="htmlview paragraph">A 4-cycle Kohler ECH-630 engine certified to the Phase 3 emissions standards was operated over the G2 test cycle, a six-mode steady-state test cycle, in its original configuration. This engine was equipped with electronic ignition, electronic fuel injection and an oxygen sensor.</div><div class="htmlview paragraph">Certification gasoline fuel was splash-blended by percent volume with ethanol and isobutanol to result in the test blend levels of E10, E15, iB16 and iB8-E10.</div><div class="htmlview paragraph">Reductions in emission rates of carbon monoxide (up to 12.0% with the ethanol blends and up to 11.4% with the isobutanol blends) were achieved along with a reduction in total hydrocarbons (up to 10.9% with the ethanol blends and up to 8.2% with the isobutanol blends). Nitrogen oxide emissions were decreased by up to 9.8% with the ethanol blends.</div><div class="htmlview paragraph">Emission rates of carbon dioxide were decreased by up to 3.2% with the ethanol blends and up to 4.2% with the isobutanol blends. Brake-specific fuel consumption was increased with E15 and iB8-E10. Increases in thermal efficiency, from 10.4% to 13.3% were noted with all alcohol fuel blends.</div><div class="htmlview paragraph">The use of E10, E15, and iB8-E10 significantly increased emissions of acetaldehyde by up to 3 times that of gasoline not containing ethanol. The E15 fuel blend decreased emissions of benzaldehyde. The iB16 and iB8-E10 fuels increased emissions of propionaldehyde, and butyraldehyde/2-methylpropanal and methacrolein.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".