Physical Properties of Sequential Interpenetrating Polymer Networks Produced from Canola Oil-Based Polyurethane and Poly(methyl methacrylate)
Bibliographic record
Abstract
Sequential interpenetrating polymer networks (IPNs) were prepared using polyurethane (PUR) synthesized from canola oil-based polyol with terminal primary functional groups and poly(methyl methacrylate) (PMMA). The properties of the material were evaluated by dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), and modulated differential scanning calorimetry (MDSC), as well as tensile properties measurements. The morphology of the IPNs was investigated using scanning electron microscopy (SEM) and MDSC. A five-phase morphology, that is, sol phase, PUR-rich phase, PUR-rich interphase, PMMA-rich interphase, and PMMA-rich phase, was observed for all the IPNs by applying a new quantitative method based on the measurement of the differential of reversing heat capacity versus temperature from MDSC, although not confirmed by SEM, most likely due to resolution restrictions. NCO/OH molar ratios (cross-linking density) and compositional variations of PUR/PMMA both affected the thermal properties and phase behaviors of the IPNs. Higher degrees of mixing occurred for the IPN with higher NCO/OH molar ratio (2.0/1.0) at PUR concentration of 25 wt %, whereas for the IPN with lower NCO/OH molar ratio (1.6/1.0), higher degrees of mixing occurred at PUR concentration of 35 wt %. The mechanical properties of the IPNs were superior to those of the constituent polymers due to the finely divided rubber and plastic combination structures in these IPNs.
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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".