Effects of aluminoxane cocatalysts on bis(imino)pyridine iron‐catalyzed ethylene oligomerization
Bibliographic record
Abstract
Abstract Ethylene oligomerization facilitated with a bis(imino)pyridine iron catalyst in the presence of various aluminoxane cocatalysts is investigated in this work. The effects of different trialkylaluminiums and [H2O]/[Al] molar ratios employed in the synthesis of aluminoxanes on the catalytic activity, selectivity to linear α‐olefins, and polymer share have been discussed. The results show that MAO‐S (methylaluminoxane synthesized herein) renders the highest catalytic activity but with a rather high undesired polymer share, and i‐BAO (iso‐butylaluminoxane) is the most optimum cocatalyst, rendering the predominant production of α‐olefins at high activity with significantly reduced polymer share. On the contrary, EAO (ethylaluminoxane) shows quite poor catalytic activity. The use of mixed aluminoxanes, MBAO (mixed aluminoxanes synthesized from trimethylaluminium and triisobutylaluminium mixtures), and EBAO (mixed aluminoxanes synthesized from triethylaluminium and triisobutylaluminium mixtures) as cocatalysts also renders different catalytic performance, indicating that the composition/structure of mixed aluminoxanes has pronounced effects on the catalytic activity and selectivity to linear α‐olefins.
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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".