Modification of epoxidised canola oil
Bibliographic record
Abstract
Abstract Epoxidation of canola oil was carried out using a peroxyacid generated in situ from hydrogen peroxide and a carboxylic acid (acetic or formic acid) in the presence of liquid inorganic acid, H2SO4, as catalyst. Acetic acid was found to be a better oxygen carrier than formic acid as it gave about 11% more conversion of ethylenic unsaturation to oxirane than that given by formic acid under otherwise identical conditions. A high temperature of above 65 °C is significantly unfavourable for achieving high oxirane numbers, as the selectivity to oxirane ring opening reaction increases. Higher concentrations (above 0.5 mol/mol of ethylenic unsaturation) of acetic acid, formic acid or hydrogen peroxide are also detrimental (mainly, the carboxylic acids) but much less than that of the temperature effect. From a detailed process developmental study, the parameters optimised were a temperature of 65 °C, acetic acid‐to‐ethylenic unsaturation molar ratio of 0.5, hydrogen peroxide‐to‐ethylenic unsaturation molar ratio of 1.5 and sulphuric acid loading of 2%. A relative conversion to oxirane of 81% and an iodine value conversion of 86.5% were obtained under the optimum reaction conditions. The formation of the epoxide as well as ring‐opened product of canola was confirmed by FTIR and 1H NMR spectral analysis. From a kinetic analysis, the activation energy of this commercially important reaction was determined to be 10.7 kcal/mol. The findings of this study show that hydroxylated canola oil can be used as a starting material for the lubricant formulations. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".