Why only half of the added ansa-metallocene catalyst active in the E/P/diene polymerization: catalyst evaluation in terms of active center [Zr]/[C*] fraction and polymerization propagation rate constants
Bibliographic record
Abstract
To understand how the silyl-bridged metallocene catalysts rac-Me2Si(2-Me-4-Ph-Ind)2ZrCl2 behave against dienes under the same reaction condition because of its importance as a commercial polymerization catalyst and ethylene propylene diene monomers (EPDM). Adding diene depressed the catalytic activity, especially 5-ethylidene-2-norbornene (ENB) exerted the most substantial deactivation effect. Firstly, we examine the (ENB, vinyl norbornene (VNB) and 4-vinylcyclohexene (VCH)) non-conjugated and conjugated (isoprene (IP), butadiene (BD)) diene and address how polymerization catalysts behave against these dienes. For example, the catalytic activity was enhanced with IP and BD (3–3.3106 gm/mmolMt·h) compared to ENB, VNB and VCH. The VNB incorporation rate was prolonged (5.4 mol%), but with IP and BD, it was relatively moderate. E/P/IP and E/P/BD with higher incorporation of E produced a higher MW, which means that the chain transfer reaction with ethylene is slower than P. Secondly, we address how the dienes exocyclic and exocyclicπ bonds of non-conjugated and conjugated properties of IP and BD affect the kinetic measurements such as active centers [Zr]/[C*] fraction, EPDM chain propagation, termination, and isomerization. Finally, we compare [Zr]/[C*] and kpPE, kpP and kpDienes for different EPDM. After collecting these kinetic parameters, we can describe the mechanism’s intricacy and the existence of considerable catalyst dormancy with dienes under identical reaction conditions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".