Copolymerization of ethylene and isoprene initiated by metallocene catalyst
Bibliographic record
Abstract
In this study, we explore the dynamic nature of metallocene catalysts during ethylene/isoprene E/IP) copolymerization, with a focus on the influence of various reaction parameters. Challenges, why do olefinic catalysts show lower activity and molecular weight (Mw) at higher temperatures and low activity with higher diene content? Firstly, we investigate the observed phenomena of decreased catalyst activity and Mw at elevated temperatures using 1.25 μmole of the metallocene. Notably, a substantial increase in active sites from 30 °C to 40 °C, reaching a peak of 89 %. Surprisingly, further temperature increments lead to a noticeable decline in active sites. At 30 °C, the active site primarily engages in the insertion of IP through 3,4 connections, with no detectable cis-1,4 or trans-1,4 connections, which suggests a lack of stereoselectivity at this temperature. At 40–50°C, cis/trans-1,4 connections and active sites are approaching a higher level. This implies a reduction in chain transfer reactions, making catalytic active sites more favorable to cis/trans-1,4 connections. Secondly, we observe a remarkable impact of IP concentration in the E/IP copolymers, active centers, and activity show stability when the amount of IP was 0.116–0.48 mol/L and then started to decline when the amount of IP 0.96 mol/L, indicating a threshold beyond which deactivation occurs. Increasing IP concentration not only reduces activity and active sites but also fails to reactivate dormant polyethylene sites. The Mw and active centers of copolymers progressively decrease with elevated IP concentration, suggesting a faster chain transfer reaction with IP compared to E.
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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".