Propylene–Ethylene Copolymer Covalent Adaptable Networks Synthesized by Resonance‐Stabilized, Radical‐Based Reactive Processing with Excellent Elevated‐Temperature Creep Resistance
Bibliographic record
Abstract
Low-crystallinity propylene-ethylene copolymer (PEC) thermoplastics exhibit creep in the melt and semicrystalline states. To enhance creep resistance while maintaining reprocessability, dynamic covalent cross-links are introduced through one-step, radical-based reactive processing to create covalent adaptable networks (CANs). During reactive processing, it is essential to suppress β-scission of propylene repeat units. To promote the formation of resonance-stabilized macroradical intermediates, a methacrylate-based cross-linker bis(4-methacryloyloxyphenyl) disulfide (BPMA) is replaced with a phenylacrylate-based cross-linker bis(4-phenacryloyloxyphenyl) disulfide (BPST) and styrene and divinylbenzene, vinyl aromatic additives, are incorporated. The use of BPST but not BPMA leads to percolated PEC CAN formation. Adding vinyl aromatic additives reduces the disparity in cross-linking capability between BPMA and BPST. The resulting PEC CANs show markedly improved elevated-temperature creep resistance compared to neat PEC. Relative to thermoplastic PEC, the best-performing PEC CAN suppresses >99% of viscous creep at 160 °C (melt state) over 600 s and >98% at 100 °C (semicrystalline state) over 10,000 s. This top-performing PEC CAN is reprocessable through compression molding and twin-screw extrusion, achieving full recovery of cross-link density and tensile properties. These results showcase a promising one-step strategy for producing recyclable PEC CANs with enhanced creep resistance in melt and semicrystalline states, addressing critical limitations of low-crystallinity polyolefins.
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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".