Effect of Cocatalysts and Solvent on Selective Ethylene Oligomerization
Bibliographic record
Abstract
Ethylene oligomerization activities of chromium catalysts stabilized by different dipyrrole-based ancillary ligands, [(Ph 2 C(C 4 H 4 N) 2 )] ( 2 ), [Ph 2 C(C 4 H 4 N)(C 5 H 6 N)] ( 3 ), [(Et) 2 C(C 4 H 4 N) 2 ] ( 4 ), and [(C 6 H 5 )(C 5 H 4 N)C(C 4 H 4 N)(C 5 H 6 N)] ( 5 ), have been investigated using different activation methods, and the results have been compared with the commercial Chevron–Phillips ethylene trimerization system. Upon activation with triethylaluminum (TEA), chromium catalysts stabilized by dipyrrole-based ligands 2 – 5 showed a lower activity and selectivity compared to the Chevron–Phillips trimerization system based on 2,5-dimethylpyrrole ( 1 ) as the ancillary ligand. However, unprecedented increases in both activity and selectivity have been observed by carrying out the oligomerization in methylcyclohexane using depleted-methylaluminoxane (DMAO) along with triisobutylaluminum (TIBA) (1:2 ratio) as cocatalyst system under mild conditions, even for the Chevron–Phillips system itself. Well-defined chromium complexes, [(Ph 2 C(C 4 H 3 N) 2 )Cr(Cl)(THF) 3 ] ( 6 ) and {[Ph 2 C(C 4 H 3 N)(C 5 H 6 N]Cr(THF)(μ-Cl)} 2 ( 7 ), have been synthesized and fully characterized. Upon activating with MAO, catalyst 7 produced a statistical distribution of oligomers, whereas under identical oligomerization conditions catalyst 6/ MAO was found to be inactive. The use of MeAlCl 2 as cocatalyst to activate 7 resulted in the switching of the catalyst’s behavior from producing a statistical distribution of LAOs to the selective trimerization of ethylene to 1-hexene. The addition of dialkylzinc along with MAO resulted in an unprecedented activity increase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".