Role of the Metal Oxidation State in the SNS−Cr Catalyst for Ethylene Trimerization: Isolation of Di- and Trivalent Cationic Intermediates
Bibliographic record
Abstract
The reaction of the highly selective [CySCH2CH2N(H)CH2CH2SCy]CrCl3 catalyst precursor with alkyl aluminum activators was examined with the aim of isolating reactive intermediates. Reaction with Me3Al afforded a cationic trivalent chromium alkyl species {[CySCH2CH2N(H)CH2CH2SCy]CrMe(mu-Cl)}2{(AlMe3)2(m-Cl}2.(C7H8)2 (1a). Although it was not possible to obtain crystalline samples of sufficient quality from the reaction with MAO (the most preferred activator), the near-to-identical EPR spectra indicated a very close structural similarity with 1a. Ethylene oligomerization tests clearly revealed that 1 and other cationic trivalent dimeric complexes {[CySCH2CH2N(H)CH2CH2SCy] CrCl(mu-Cl)}2{AlCl4}2.(C7H8)1.5 (2), monomeric [(CySCH2CH2N(H)CH2CH2SCy)CrCl2 (THF)][AlCl4] (3), and {[CySCH2CH2N(H)CH2CH2SCy]Cr(eta2-AlCl4)}{Al2Cl7} (4) adducts display the same catalyst selectivity as the [CySCH2CH2N(H)CH2CH2SCy]CrCl3 complex and, therefore, are probably all precursors to the same catalytically active species. 2, 3, and 4 were obtained upon treatment of [CySCH2CH2N(H)CH2CH2SCy] CrCl3 with different stoichiometric ratios of AlCl3.. When i-BAO activator was used, reduction of the metal center occurred readily, affording {([CySCH2CH2N(H)CH2CH2S Cy]Cr)(mu-Cl)]2}{(i-Bu)2AlCl2}2 (5). 5 is also a selective catalyst, thus indicating that trivalent species are most probably precursors to a divalent catalytically active complex. Reaction of CrCl2(THF)2 with the ligand afforded the labile divalent adduct [CySCH2CH2N(H)CH2CH2SCy]CrCl2(THF) (6), also catalytically active and selective. Instead, deprotonation of the ligand with n-BuLi followed by reaction with CrCl2(THF)2 gave the dinuclear complex [(mu-CySCH2CH2NCH2CH2SCy)CrCl]2 (7), which did not produce oligomers.
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 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 | 0.001 |
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".