Effect of green fuel and green lubricant with metallic nanoparticles on emissions of HC, CO, NOx, and smoke for a compression ignition engine
Bibliographic record
Abstract
Abstract The United Nations Sustainable Development Goals (SDGs) are imperative from the point of view of protecting the environment by employing sustainable options. Considerable research has been carried out in the transportation sector to meet this objective. Here, the influence is assessed of epoxidised gingelly oil methyl ester biolubricant with alumina (Al 2 O 3 ) nanoparticles on the performance and emissions of a single cylinder 0.66-L capacity direct injection compression ignition engine driven by gingelly B20 biodiesel. Engine tests are carried out with gingelly B20 biodiesel as a fuel, and gingelly methyl ester (B100), epoxidised gingelly methyl ester (B100E), and epoxidised gingelly methyl ester (B100E) mixed with 0.5%, 1.0%, and 1.5% w/w alumina (Al 2 O 3 ) nanoparticles as the lubricant combinations. The results are compared with baseline B20 biodiesel fuel-mineral lubricant operation. The findings indicate that brake thermal efficiency increases by 8.64% for epoxidised gingelly methyl ester (B100E) with 1.0% w/w alumina (Al 2 O 3 ) nanoparticle biolubricant in comparison to baseline operation. Considerable reductions in emissions are detected; specifically, reductions of 52.4%, 22.0%, 20.0%, and 34.9%, respectively, are observed for CO, NO x , and HC concentrations and smoke opacity for the abovementioned combination as compared to baseline operation. The present work suggests that further research is merited on green fuel-green lubricant combinations. The findings of this study address the United Nations Sustainable Development Goals (SDGs) 7 and 13.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".