Synthesis and Rheological Properties of Long-Chain-Branched Isotactic Polypropylenes Prepared by Copolymerization of Propylene and Nonconjugated Dienes
Bibliographic record
Abstract
Long-chain-branched isotactic polypropylenes (LCBed PP) were synthesized by copolymerizing propylene with a small amount of nonconjugated α,ω-diene (1,9-decadiene or 1,7-octadiene) using the catalyst system of rac -Me 2 Si(2-MeBenz[ e ]Ind) 2 ZrCl 2 (MBI)/MMAO. In this approach, the LCB structures were introduced by the incorporation of in situ generated macromonomers with pendant 1-octenyl or 1-hexenyl groups during the polymerization. A detailed study on the effects of diene concentration on polymer properties was conducted. Polymer chain microstructures were characterized by 13 C NMR, GPCV, and DSC. In the propylene/1,9-decadiene copolymerization, a series of LCBed polymer samples with the long-chain-branch density (LCBD) of up to 0.53 branch structures per 1000 carbons were produced with the diene concentrations of 0.177−3.54 mmol/L at 40 and 25 °C. A diene concentration of 35.4 mmol/L yielded cross-linked polymer gels. In the copolymerization of propylene and 1,7-octadiene, in addition to a small fraction of LCB structures produced, a cyclic seven-member ring structure was observed due to the cycloaddition of 1,7-octadiene. The cyclization significantly decreased the LCBD in the polymers. A small-amplitude oscillatory shear flow measurement was conducted to evaluate the rheological properties of the LCBed polymers. Compared to the linear samples prepared at the same polymerization conditions, the LCBed polymers exhibited enhanced low-frequency complex viscosity, improved shear-thinning, increased dynamic moduli, and reduced phase angle. The samples also showed thermorheological complexity and enhanced activation energy at low frequencies. These particular properties are related to the LCB in the polymers and become more significant with the increase of LCBD. The LCBed polypropylenes were also blended with their counterpart linear samples and demonstrated the improvement of rheological properties.
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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".