Production of Polyols from Canola Oil and their Chemical Identification and Physical Properties
Bibliographic record
Abstract
Abstract The feasibility of a method based on ozonolysis and hydrogenation reactions for the production of polyols from unsaturated canola oil has been demonstrated. Polyol products with primary alcohol functional groups at position nine of each fatty acid ester in the original triacylglycerol have been produced from canola oil. Short straight‐chain alcohols were also produced and were removed by wiped‐blade molecular distillation. The pure components of the polyol, i.e. mono‐ol, diol and triol were separated by flash chromatography, and identified by Fourier‐transform infrared (FTIR), 1H‐nuclear magnetic resonance (NMR), 13C‐NMR as well as mass spectrometry. Polyol identification was facilitated by the use of a simple high‐performance liquid chromatography (HPLC) method to determine the composition of the polyol mixture, which can be exploited as a quality‐control mechanism in designing novel polyol feedstocks. Basic correlations were established between the molecular diversity of the polyols and their physicochemical properties, such as hydroxyl number, acidity number, and viscosity. It has been found that the produced polyols are suitable for processing methods employing polyols for the production of polyurethanes and can be manipulated to create polyurethanes with desirable properties.
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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 | 0.001 | 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".