Physical Properties of Canola Oil Based Polyurethane Networks
Bibliographic record
Abstract
A new generation polyol (generation-II) with significantly higher triol content and higher hydroxyl value was synthesized from canola oil by introducing a mild solvent (ethyl acetate) and a more efficient reductive reagent (zinc) to the previous synthetic procedure (Narine, S. S.; Yue, J.; Kong, X. J. Am. Oil Chem. Soc. 2007, 84, 173-179). Polyurethane (PUR) elastomers were prepared by reacting this type of polyol with aliphatic diisocyanates. The physical and thermal properties of the PUR elastomers were studied using dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC) and compared to the elastomers made from the old generation polyol (generation-I). The concentration of elastically active network chains (nue) of the polymer networks was calculated based on rubber elasticity theory. Larger nue and narrower distribution of nue was observed in the case of the PURs prepared from the generation-II polyol. The relatively faster relaxation at higher temperature for this type of PUR elastomer, suggests a tighter cross-linked network structure by reducing the dangling chains effect. With the same OH/NCO molar ratio, the PURs prepared from the generation-II polyol showed higher glass transition temperatures (Tg), higher Young's modulus and tensile strength, and longer elongation at break.
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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) | 0.000 | 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 | 0.000 |
| 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".