Evaluating Esters Derived from Mustard Oil (<i>Sinapis alba</i>) as Potential Diesel Additives
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Biodiesel was produced from mustard oil utilizing transesterification with methanol, ethanol, propanol, and butanol to evaluate the characteristics of mustard biodiesel as an additive to regular diesel. Mustard oil was transesterified with alcohol at 6:1 alcohol to oil molar ratio, using KOH as a catalyst at 1 wt%. The maximum ester content achieved by this method was only 66%. Distillation was then used to purify the ester, raising the ester content to 99.8%. Alternatively, mustard oil methyl ester (MME) can be mixed with esters derived from canola oil or soybean oil to achieve an ASTM quality biodiesel. Biodiesel derived from mustard showed great potential as lubricity additive for regular diesel fuel. With an addition of 1% MME, lubricity of diesel fuel was improved by 43.7%. It is also found that methyl ester is the best lubricity additive among all esters (methyl‐, ethyl‐, propyl‐, and butyl‐ester). MME can be used at −16 °C without freezing whereas monounsaturated compounds (oleic, eicosenoic, and erucic esters) largely present in esters derived from mustard oil can tolerate −42 to −58 °C. Monounsaturated esters derived from higher alcohols such as butyl alcohol demonstrated a superior low temperature tolerance (−58 °C) as compared to that derived from lower alcohol such as methyl alcohol (−42 °C).
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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 | 0.001 |
| 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