Two-State Models for Olefin Polymerization using Metallocene Catalysts. 1. Application to Fluxional Metallocene Catalyst Systems
Bibliographic record
Abstract
A kinetic model was developed to describe the propylene polymerization behavior of fluxional, two-state metallocene catalysts. In particular, the pentad and molecular weight distributions can be described as well as other parameters of interest, such as the weight fraction of crystallizable sequences and the isotactic sequence length distribution, in terms of fundamental kinetic constants and polymerization conditions that pertain to these two-state catalyst systems. The model was used in an attempt to describe the polymerization behavior of two, prototypical, fluxional catalyst systems, (2-PhInd) 2 ZrCl 2 /MAO ( 1 ) and (2- p -CF 3 PhInd) 2 ZrCl 2 /MAO ( 2 ). The model can accurately reproduce the pentad distributions observed in PP prepared using these catalysts and the response of the distribution to changes in polymerization conditions, specifically changes in [C 3 H 6 ] at constant T . These studies illustrate that the rate of state-to-state interconversion is slower but of comparable magnitude to the rate of monomer insertion and that the states have similar stability and reactivity. The broad molecular weight distributions previously observed with this family of catalysts can be described by the model. However, the model predicts that the state-to-state interconversion rate has to be significantly slower than the rate of formation of dead polymer chains, and this is inconsistent with the rate estimated from the response of the pentad distribution to changes in the rate of propagation (i.e., [C 3 H 6 ]). Recent work where propylene polymerizations using 1 were carried out to low conversion indicate that the broad MWD seen in earlier studies is partly related to variations in [C 3 H 6 ] during polymerization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".