Effects of Poly(propylene glycol)‐Based Triamine on the Sol/Gel Curing and Properties of Hybrid Non‐Isocyanate Polyurethanes
Bibliographic record
Abstract
Abstract Non‐isocyanate polyurethanes (NIPUs) synthesized from biodiesel‐derived diglycerol dicarbonate (DGC) and triamine functional poly(propylene glycol) (Jeffamine) are investigated to improve upon poor water resistance and slow curing kinetics observed in (DGC/diamine functional Jeffamine) NIPUs. The structural, thermal, and rheological properties of the prepolymer formulations match those of their diamine‐prepared analog previously reported. The NIPU prepolymers (number average molecular weight, MNbetween 4200 and 6400 g mol−1) are subsequently functionalized with trimethoxy silane end‐cappers to yield moisture‐curable hybrid NIPUs. The prepolymers are both successfully end‐capped and subsequently cured under ambient conditions. The sol/gel curing kinetics of the prepolymers are extensively studied, and the effects of the capping methodology, moisture, and the amount of catalyst are quantified so that gel times are observed in as little as 1.4 h at room temperature. The developed curing methods are effective leading to final hybrid films with high gel contents (>86%). The tensile properties of the final films resulting are considerably better compared to previously reported DGC/Jeffamine‐based NIPUs, which are too soft to undergo mechanical testing. Also, the films are found to exhibit a tenfold decrease in water absorption compared to the latter (5% vs 70%, respectively).
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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".