An assessment of various nanoadditives and tribocorrosion with waste cooking biodiesel fueled in a diesel engine
Bibliographic record
Abstract
The steady-state coefficient of friction rate of waste cooking biodiesel fuel blends is higher for B90 (18.2%), B60 (7.2%), B20 (16.72%), B10 (30.8%), and diesel (38.77%) than for the B40 fuel blend, and the wear scar diameter of the B40 to B100 fuel blends has a minimal range of 0.5 mm. The flash temperature parameter was the highest for the B40 to B100 fuel blends, and the corrosion rate was the lowest for the B40 and B50 fuel blends. Subsequently, the B40 (40% waste cooking oil + 60% diesel fuel) fuel blend was chosen, along with cerium (25 ppm), zinc (25 ppm), and titanium nanoparticles (25 ppm) as fuel additives. The B40 + D60 + titanium (25 ppm) fuel blend resulted in an improved thermal break efficiency and 3.83% lower brake thermal energy consumption than diesel fuel. The B40 + D80 + titanium (25 ppm) fuel blend resulted in a 2.08% reduction in HC, 36.36% reduction in CO, and 16.25% reduction in smoke emissions, along with marginally higher (8.5%) NOx emissions than diesel fuel. In addition, for the B40 + D80 + titanium (25 ppm) fuel blend, the combustion characteristics of cylinder pressure (58.82 bar) and HRR (66.65 J/deg CA) were the same as those of diesel fuel at peak load.
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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".