Exploring the Effect of Poly(propylene carbonate) Polyol in a Biobased Epoxy Interpenetrating Network
Bibliographic record
Abstract
Poly(propylene carbonate) (PPC) polyol derived from carbon dioxide has been used to make a tough biobased interpenetrating polymer network (IPN). PPC polyol (10, 20, and 30 phr) was added to an epoxy/poly(furfuryl alcohol) IPN, and the effect of PPC polyol on the tensile modulus, tensile strength, tensile toughness, and notched Izod impact strength was determined. Dynamic mechanical analysis (DMA) was used to investigate the effect of PPC polyol on the glass-transition temperature. Loss area (LA) as a characteristic of IPN damping properties was evaluated using DMA. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) were used to obtain more information on the morphology of IPNs on the micro- and nanoscale. It was found that the incorporation of PPC polyol improved the notched Izod impact strength and tensile toughness up to 190 and 220%, respectively. The damping factor peak was broadened with the addition of PPC polyol, and the glass-transition temperature was decreased as the amount of PPC polyol increased. The IPN with 20 phr PPC polyol exhibited better damping properties than neat epoxy and the epoxy/PFA IPN. SEM and AFM images revealed that PPC polyol domains were dispersed in the epoxy phase with an average diameter of around 280 nm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".