Microstructural Characterization of Molecular Weight Fractions of Ethylene/1,7‐Octadiene Copolymers Made with a Constrained Geometry Catalyst
Bibliographic record
Abstract
Abstract Summary: Ethylene was copolymerized with 1,7‐octadiene (OD) using a methylaluminoxane (MAO) activated constrained geometry catalyst (dimethylsilyl(N‐tert‐butylamido)(tetramethylcyclopentadienyl)titanium dichloride; CGC‐Ti) at 140 °C in toluene. The polymerization activity increased with the addition of small amounts of OD, reached a maximum, and then decreased at higher OD concentrations. The vinyl‐bond content of the polymers increased with increasing diene in the feed. Increasing OD concentrations led to the production of long chain branched (LCB) polyethylene (PE). Both 1,3‐cycloheptane (CY7) and 1,5‐cyclononane (CY9) units were identified in the copolymers, with the dominant CY9 structure accounting for approximately 53% percent of all rings. Selected copolymers were fractionated by molecular weight using a solvent/non‐solvent technique to obtain their detailed microstructure. The relative amounts of CY7 and CY9 structures were not a function of molecular weight. The number of vinyl functionalities decreased with increasing molecular weight, while the number of branches increased, indicating the incorporation of macromonomers with pendant or terminal vinyl groups. Molecular weight distributions of ethylene homopolymer and three fractions. magnified image Molecular weight distributions of ethylene homopolymer and three fractions.
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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".