Mono‐ and binuclear nickel catalysts for 1‐hexene polymerization
Bibliographic record
Abstract
Polymerization of 1‐hexene was carried out using a mononuclear (MN) catalyst and two binuclear (BN 1 and BN 2 ) α‐diimine Ni‐based catalysts synthesized under controlled conditions. Ethylaluminium sesquichloride (EASC) was used as an efficient activator under various polymerization conditions. The highly active BN 2 catalyst (2372 g poly(1‐hexene) (PH) mmol −1 cat) in comparison to BN 1 (920 g PH mmol −1 cat) and the MN catalyst (819 g PH mmol −1 cat) resulted in the highest viscosity‐average molecular weight ( M v ) of polymer. Moreover, the molecular weight distribution (MWD) of PH obtained using BN 2 /EASC was slightly broader than those obtained using BN 1 and MN (2.46 for BN 2 versus 2.30 and 1.96 for BN 1 and MN, respectively). These results, along with the highest extent of chain walking for BN 2 , were attributed to steric, nuclearity and electronic effects of the catalyst structures which could control the catalyst behaviour. Differential scanning calorimetry showed that the glass transition temperatures of polymers were in the range − 58 to −81 °C, and broad melting peaks below and above 0 °C were also observed. In addition, longer α‐olefins (1‐octene and 1‐decene) were polymerized and characterized, for which higher yield, conversion and molecular weight were observed with a narrower MWD. The polymerization parameters such as polymerization time and polymerization temperature showed a significant influence on the productivity of the catalysts and M v of samples.
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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".