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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".