A novel alternative fuel mixture (diesel–biodiesel–pentanol) for the existing unmodified direct injection diesel engine: performance and emission characteristics
Bibliographic record
Abstract
The present study investigated the performance and emission characteristics of a single cylinder direct injection diesel engine fuelled with diesel – jatropha biodiesel – pentanol blends. The test fuels used for the experiment include diesel fuel (Diesel), 80% diesel (v/v) – 20% jatropha biodiesel (v/v) (D80J20), 70% diesel (v/v) – 20% jatropha biodiesel (v/v) – 10% pentanol (v/v) (D70J20P10), and 60% diesel (v/v) – 20% jatropha biodiesel (v/v) – 20% pentanol (v/v) (D60J20P20). Studied performance characteristics include brake specific fuel consumption and torque, while emission characteristics include carbon monoxide, nitrogen oxides, and smoke opacity. Experimental results revealed that the addition of pentanol influenced a reduction in brake power and torque with a noticeable improvement in engine exhaust emissions. To conclude, the addition of pentanol (20%, v/v) to diesel–jatropha blends resulted in lowered CO, NOx, and smoke opacity by 41.76%, 27.6%, and 32.4%, respectively, because of improved oxygen content of the resulting ternary mixture and improved combustion efficiency.
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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 | 0.000 |
| 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".