Two-State Models for Propylene Polymerization Using Metallocene Catalysts. 2. Application to <i>a</i><i>nsa</i>-Metallocene Catalyst Systems
Bibliographic record
Abstract
A kinetic model to describe the propylene polymerization behavior of ansa -metallocene catalysts was derived. The model can predict n -ad stereosequence distributions, polymer crystallinity, and related properties as well as distinguish between extremes in kinetic behavior expected for such catalysts. In particular, where polymer microstructure is sensitive to changes in [C 3 H 6 ], the model can provide reliable estimates of kinetic parameters of interest, including ratios between rates of some of the significant reaction steps involved in polymer microstructure formation. The model is applied to a description of the polymerization behavior of some simple symmetrical [Me 2 C(Cp)(Flu)MCl 2; M = Zr, Hf] and unsymmetrical [Me 2 Y(Cp)(Ind)MCl 2; M = Zr, Hf; Y = C, Si] ansa -metallocene catalysts, activated with methyl aluminoxane. With the former two catalysts, the Zr catalyst operates very close to the kinetic quenching limit where chain inversion (or chain back-skip) is slow compared to monomer insertion, while for the Hf analogue, these two processes have more comparable rates. In the more complicated, unsymmetrical systems, both Zr- and Hf-based systems (Y = Si) operate under conditions where inversion is much faster that propagation, whereas for the Hf catalyst (Y = C), intermediate behavior is observed, and the corresponding Zr complex (Y = C) produces poly(propylene) where propagation is faster than inversion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".