Catalytic Oligomerization of Ethylene to Higher Linear α-Olefins Promoted by the Cationic Group 4 [(η<sup>5</sup>-Cp-(CMe<sub>2</sub>-bridge)-Ph)M<sup>II</sup>(ethylene)<sub>2</sub>]<sup>+</sup>(M = Ti, Zr, Hf) Active Catalysts: A Density Functional Investigation of the Influence of the Metal on the Catalytic Activity and Selectivity
Bibliographic record
Abstract
A detailed theoretical analysis is presented of the catalytic abilities of heavier group 4 (M = Zr, Hf) metals for linear ethylene oligomerization with the cationic [(eta(5)-C(5)H(4)-(CMe(2)-bridge)-C(6)H(5))M(IV)(CH(3))(2)](+) complex as precatalyst, employing a gradient-corrected DFT method. The parent Ti system has been reported as a highly selective catalyst for ethylene trimerization. The mechanism involving metallacycle intermediates, originally proposed by Briggs and Jolly, has been supported by the present study to be operative for the investigated class of group 4 catalysts. Metallacycle growth through bimolecular ethylene uptake and subsequent insertion is likely to occur at uniform rates for larger cycles that are furthermore comparable for Ti, Zr, and Hf catalysts. Ethylene insertion into the two smallest five- and seven-membered cycles is found to become accelerated for Zr and Hf catalysts, which is due to geometrical factors. In contrast, electronic effects act to raise the barrier for metallacycle decomposition, affording alpha-olefins upon descending group 4. This process is furthermore predicted to be kinetically more difficult for larger metallacycles. The oligomer distribution of the Zr-mediated reaction is likely to comprise predominantly 1-hexene together with 1-octene, while 1-butene and alpha-olefins of chain lengths C(10)-C(18) should occur only in negligible portions. A similar composition of alpha-olefins having C(6)-C(18) chain lengths is indicated for the Hf catalysts, but with long-chain oligomers and polymers as the prevalent fraction. Between the group 4 catalysts of the investigated type, the Zr system appears as the most promising candidate having catalytic potential for production of 1-octene, although not selectively. The influence of temperature to modulate the oligomer product composition has been evaluated.
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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) | 0.002 | 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".