Evaluating Esters Derived from Mustard Oil (<i>Sinapis alba</i>) as Potential Diesel Additives
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).
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.
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 | 0.001 | 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".