Mechanisms of branch formation in metal‐catalyzed ethene polymerization
Bibliographic record
Abstract
Abstract Branches are an important aspect of the structure of real polyethylene. Branches can be short (Me, Et) or longer; long‐chain branches (LCB, >100 carbons), in particular, are important because they can have a dramatic effect on polymer properties. In this review, we summarize mechanistic information from organometallic and computational chemistry and use this to examine the most probable sources of each type of branch. Short branches can be introduced deliberately by copolymerization with an α‐olefin (possibly formed in situ from ethene). Me branches may be formed by one‐carbon chain walking and propagation, and/or from insertion of an oligomer/macromer in an MMe bond formed via chain transfer to the cocatalyst [Me3Al or methylaluminoxane (MAO)]. Et branches are most likely formed through β‐hydrogen transfer to ethene, followed immediately by reinsertion of the newly formed macromer. LCBs have usually been ascribed to reinsertion of macromers. However, certain catalysts exhibit LCB formation patterns that are hard to reconcile with this model, and a ‘two‐monomer’ model was recently proposed to explain the observations for these systems. In this review, we present an alternative explanation (chain walking) that would fit the same facts for these catalysts. © 2011 John Wiley & Sons, Ltd. This article is categorized under: Structure and Mechanism > Molecular Structures
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".