Physical Properties of Polyurethanes Produced from Polyols from Seed Oils: I. Elastomers
Bibliographic record
Abstract
Abstract Polyols synthesized by ozonolysis and hydrogenation from canola oil were reacted with aliphatic 1,6‐hexamethylene diisocyanates (HDI) to produce polyurethane (PU) elastomers. The properties of the materials were examined by dynamic mechanical analysis (DMA), thermomechanical analysis (TMA), modulated differential scanning calorimetry (MDSC), and thermogravimetric analysis (TGA), and measurements were taken of tensile properties. The effect of dangling chains on network properties was assessed. The formation of hydrogen bonds was observed by FTIR. The measured properties were found to be strongly dependent on processing‐dependent factors such as the crosslinking density and the molar ratio of polyols to HDI component. The glass transition temperatures (Tg) of the elastomers were found to increase as the OH/NCO molar ratio decreased. With the same OH/NCO molar ratio, Tg of canola‐oil‐based PU was higher than that of soybean‐oil‐based PU. The TGA thermographs showed two well‐defined steps of degradation for all the elastomers. In the first step, up to 30% weight loss, the fastest rate of loss was found at 345 °C for canola‐oil‐based PU while soybean‐oil‐based PU lost most of the weight in the second step. With the same OH/NCO molar ratio, the elastomers made from canola‐oil‐based polyol showed slightly higher Young's modulus and tensile strength.
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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".