Preparation and evaluation of vegetable oil derived biodiesel esters as lubricity additives
Bibliographic record
Abstract
Abstract Various vegetable‐oil derived esters using methyl, ethyl, butyl, and 2‐propyl alcohols were prepared and tested as diesel fuel lubricity additives in a roller‐on‐cylinder lubricity evaluator. At1% additive treat rate, the canola methyl and 2‐propyl esters, the best‐performing esters, increased the lubricity number of a reference fuel by 60%. Statistical analyses indicated strong effects of fatty acids and alcohols on the wear, friction, and lubricity number of the fuel. While no single fatty acid was identifiably responsible for the wear reduction, certain non‐linoleic compounds correlated with the wear data in a semi‐log relationship. The low‐temperature behaviour of the biodiesel was studied using differential scanning calorimetry. The major transitional peak temperature of the biodiesel esters changed with the melting points of their primary fatty acids, and decreased with the lengthening and branching of the tail alkyl groups.
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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 | 0.000 | 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 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".