Performance, evaluation and gas emission of diesel and distilled biodiesel blends in IC engine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The greenhouse gas emissions caused by burning fossil fuels are a global issue. Nowadays, biodiesel has been shown to be a good alternative to substitute totally or partially diesel to minimize GHG emissions formed during the combustion of diesel fuel in engines. This research investigates the usability of distilled biodiesel–diesel blends in a diesel engine. The distilled biodiesel was produced from soybean and coconut oil, and each fraction’s composition was characterized by gas chromatography–mass spectrometer. The soybean biodiesel light fraction was shown to be rich in compounds with up to 17 carbons, while the coconut biodiesel light fraction contained compounds with up to 13 carbons. All blends evaluated were within the density range of commercial diesel (0.82 to 0.85 g.cm−3). The consumption and emissions experiments were performed on a 1-cylinder, 4-stroke diesel engine at various loads to evaluate the influence of distilled biodiesel from soybean and coconut oil. For all blends, adding distilled biodiesel, both from soybean and coconut oil, increased the brake thermal efficiency up to 56.08% and reduced the specific fuel consumption up to 18.33% compared to diesel fuel. In addition, all distilled biodiesel–diesel blends reduced carbon monoxide emissions by up to 30%.
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.
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 | 0.000 | 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) | 0.000 | 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 it