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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".