Catalytic Oligomerization of Ethylene to Higher Linear α-Olefins Promoted by Cationic Group 4 Cyclopentadienyl-Arene Active Catalysts: Toward the Computational Design of Zirconium- and Hafnium-Based Ethylene Trimerization Catalysts
Bibliographic record
Abstract
A detailed computational exploration is presented of the catalytic abilities of heavier group 4 (M = Zr, Hf) mono(boratabenzene-arene) compounds for linear ethylene oligomerization with the cationic [(η 6 -BC 5 H 5 )-(bridge)-C 6 H 5 )M II (C 2 H 4 ) 2 ] + complex as active catalyst species, employing a gradient-corrected DFT method. The influence of the boron substitution on the cyclopentadienyl moiety and the length of the boratabenzene-arene connecting bridge on the energy profile of the oxidative coupling and the competing metallacycle growth and decomposition steps has been elucidated. This allowed us to suggest promising modifications of the parent Cp-based Ti analogue, which has been described by Hessen and co-workers as a catalyst for ethylene trimerization, thereby contributing to the computer-based rational design of improved group 4 oligomerization catalysts. The boratabenzene Zr compound bearing a CMe 2 -bridge is indicated to be an efficient trimerization catalyst, which should exhibit an activity that exceeds what is reported for the established Ti system. The computational probing reveals for the Hf counterpart a catalytic ability that is different. This system is suggested to possess catalytic potential for production of 1-octene besides the prevalent 1-hexene oligomer product. Electronic modification of the substituent on boron can act to modulate the α-olefin product composition toward an enhanced 1-octene portion, although not as the predominant product.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".