Impact of Ethanol and Isobutanol Gasoline Blends on Emissions from a Closed-Loop Small Spark-Ignited Engine
Bibliographic record
Abstract
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. 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. 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. 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. 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. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".