Dibenzylzirconium Complexes of Chelating Aminodiolates. Synthesis, Structural Studies, Thermal Stability, and Insertion Chemistry
Bibliographic record
Abstract
Aminodiolate ligands, RN(CH 2 CH 2 C(O)R‘ 2 ) 2 ( 1a − e ), allow the isolation of soluble, monomeric zirconium dialkyl complexes, [RN(CH 2 CH 2 C(O)R‘ 2 ) 2 ]ZrR‘ ‘ 2 ( 2a − e, 5, 6 ). The fluxional behavior and thermal stability of these complexes are strongly dependent on the nature of the substituents at the nitrogen center, with smaller substituents (R = Me; 2a, b, 5, 6 ) increasing the rigidity and thermal stability of the complexes. Complexes bearing tert -butyl groups ( 2c, d ) readily undergo thermal decomposition by elimination of isobutene. Thermal ortho metalation of the chiral complex Zr[(( S )-PhC(H)Me)N(CH 2 CH 2 C(O)Me 2 ][CH 2 Ph] 2 ( 2e ) affords the chiral metallacycle Zr[N{CH 2 CH 2 C(O)Me 2 } 2 {(( S )-2-C 6 H 4 C(H)Me}][CH 2 Ph] ( 7 ), which has been structurally characterized. Reaction of 7 with 1 equiv of aryl aldehyde (ArC(O)H; Ar = Ph, β -naphthyl) results in regiospecific insertion of the aldehyde into the phenyl−zirconium bond. The resulting aminotriolate complex Zr[N{CH 2 CH 2 C(O)Me 2 } 2 {(( S )-2-(( R )-( β -naphthyl)CH(O))-C 6 H 4 C(H)Me}][CH 2 Ph] ( 8b ) is formed in 91% de and has been characterized by X-ray crystallography. Further insertion of ArC(O)H into the remaining Zr−benzyl bond of 8b proceeds with poorer stereochemical control. Complex 7 also catalyzes the slow cyclotrimerization of phenylacetylene to 1,2,4- and 1,3,5-triphenylbenzene (2.5 turnovers/day). Complexes 2b, d and 7 function as precatalysts for ethylene polymerization when treated with MAO activator, although the activity is very low.
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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 | 0.000 |
| 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) | 0.106 | 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".