Enhancing the Crystallization Behavior of Isotactic Polypropylene via Trace Amounts of Olefinic (Co)polymers
Bibliographic record
Abstract
Two synthetic strategies were developed to prepare macromolecular nucleating agents: (i) in situ homopolymerization of olefin monomers (vinylcyclohexane (VCH), 7-octenyltrichlorosilane (OctCS), dichloro[bis(5-hexenyl)]silane (Hex 2 CS), and dichloro[bis(7-octenyl)]silane (Oct 2 CS)) during the prepolymerization of Ziegler–Natta(Z-N) catalysts; and (ii) their in situ copolymerization with propylene under identical catalytic conditions. Specially, copolymers of olefin monomer and propene were first used as nucleating agents for the i PP sample. The resulting Z-N catalysts, now functionalized with macromolecular nucleating agents, were subsequently employed to synthesize isotactic polypropylene ( i PP) via propylene polymerization. Subsequently, the nucleation efficiency of these macromolecular agents in the obtained i PP was systematically investigated to elucidate their impact on crystallization behavior and polymer properties. Notably, the incorporation of both homopolymer and copolymer nucleating agents significantly enhanced the crystallization temperature ( T c ) of the synthesized i PP samples, which increased to 118–127 °C─representing a maximum rise of 10 °C compared to neat i PP. Moreover, the polarized optical microscopy (POM) results revealed that the incorporation of macromolecular nucleating agents significantly accelerated the crystallization kinetics of i PP, yielding smaller and more uniform α-form spherulites compared to neat i PP. It is highlighted that structural and mechanical benefits conferred by optimized nucleating agents offer insights into the development of high-performance i PP materials.
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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".