Ozonolysis of Canola Oil: A Study of Product Yields and Ozonolysis Kinetics in Different Solvent Systems
Bibliographic record
Abstract
Abstract The use of ozonolysis has been proposed as a step in the production of vegetable‐oil‐based polyols as replacements for the equivalent petrochemicals. As part of an evaluation of the commercial viability of ozonolysis, the intermediates and products formed from the ozonolysis of canola oil using different protic and aprotic solvents and solvent mixtures were systematically studied by GC‐FID and size‐exclusion chromatography with refractive index detection (SEC‐RI). It was found that the use of an aprotic solvent (ethyl acetate) leads to the formation of oligomeric ozonolysis products whereas alcohols and/or mixtures of ethyl acetate with alcohols for the ozonolysis of canola oil do not form high molecular weight compounds. In addition, when ethyl acetate is used as the solvent, the formation of carboxylic acids is observed in the early stages of ozonolysis whereas the use of alcohols significantly reduces acid formation. As expected, extending the ozonolysis time led to extensive carboxylic acid formation, especially using ethyl acetate as a solvent. It was found that the optimum time for the complete ozonolysis of canola oil was largely independent of the solvent used for ozonolysis. However, the yield of ozonolysis products differs considerably depending on the solvents employed. Overall, a clear correlation between the ozonolysis time, product yields and the reaction exothermicity was observed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".