Solid-State <sup>91</sup>Zr NMR Spectroscopy Studies of Zirconocene Olefin Polymerization Catalyst Precursors
Bibliographic record
Abstract
(91)Zr (I = 5/2) solid-state NMR (SSNMR) spectra of the zirconocene compounds, Cp(2)ZrCl(2), Cp*(2)ZrCl(2) (1), Cp(2)ZrBr(2) (2), (Me(3)SiC(5)H(4))(2)ZrBr(2) (3), O(Me(2)SiC(5)H(4))(2)ZrBr(2) (4), (1,3-C(5)H(3))(SiMe(2)OSiMe(2))(2)(1,3-C(5)H(3))ZrBr(2) (5), Ind(2)ZrCl(2) (6), Cp(2)ZrMeCl (7), Cp(2)ZrMe(2) (8), and [Cp(2)ZrMe][MeB(C(6)F(5))(3)] (9) have been acquired. Static (91)Zr SSNMR spectra have been acquired for all complexes at magnetic fields of 9.4 and 21.1 T. Cp(2)ZrCl(2) and complexes 1 to 5 possess relatively narrow central transition powder patterns which allows for magic-angle spinning (MAS) (91)Zr solid-state NMR spectra to be acquired at a moderate field strength of 9.4 T. Complexes 6 to 9 possess ultrawideline central transition SSNMR spectra necessitating piece-wise acquisition techniques. From the static and MAS (91)Zr SSNMR spectra, it is possible to measure (91)Zr electric field gradient (EFG) and chemical shift (CS) tensor parameters, as well as the Euler angles which describe their relative orientation. Basis sets and methods for the accurate quantum chemical calculation of (91)Zr EFG and CS tensors have been identified. The origin of the observed EFG and CS tensor parameters are further investigated by visualization of the EFG and CS tensor orientations within the molecular frames. Correlations between the observed and calculated NMR tensor parameters and molecular symmetry and structure are made. All of these observations suggest that (91)Zr SSNMR spectroscopy can be utilized to probe the molecular structure of a variety of homogeneous and heterogeneous olefin polymerization catalysts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".