A review on the effects of ethanol/gasoline fuel blends on NOX emissions in spark-ignition engines
Bibliographic record
Abstract
Ethanol can be used as an alternative fuel for spark-ignition (SI) engines to increase the octane number and oxygen content of ethanol/gasoline blends, thereby reducing dependence on fossil fuels and the exhaust emissions of incomplete combustion products. Although it is widely agreed that ethanol can reduce CO and HC exhaust emissions, the literature on ethanol and NOX emissions is far from conclusive; hence there is a need for an in-depth, updated review of ethanol/gasoline blends in SI engines and the relative production of NOX emissions. In light of that, the present work aims to provide a comprehensive literature review on the current state of ethanol combustion in SI engines to shed definitive light on the potential changes in NOX emissions under various operating conditions. The first part of this paper discusses the feasibility of ethanol as an alternative transportation fuel, including world production and ethanol production processes. The physicochemical properties of ethanol and gasoline are then compared to analyze their effects on combustion efficiency and exhaust emissions. Then, the pathways of NOX formation inside the cylinder of SI engines are discussed in depth. Finally, we review and critically discuss the effects of ethanol concentration in blends and different engine parameters on NOX formation.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".