(Py)<sub>2</sub>Co(CH<sub>2</sub>SiMe<sub>3</sub>)<sub>2</sub> As an Easily Accessible Source of “CoR<sub>2</sub>”
Bibliographic record
Abstract
(Py) 2 CoR 2 (R = CH 2 SiMe 3 ) is easily prepared from (Py) 4 CoCl 2 and RLi. It is fairly stable at room temperature and serves as a convenient source of CoR 2 for transfer to other ligands. Unfortunately, (Py) 2 CoR 2 was obtained only as an oil, but the structure of the related complex (Py) 2 CoR′ 2 (R′ = CH 2 CMe 2 Ph) could be confirmed by a single-crystal X-ray diffraction study. Transfer of the CoR 2 fragment from (Py) 2 CoR 2 or (TMEDA)CoR 2 to diiminepyridine-type ligands ( 1 − 6 ) was studied as a function of ligand steric and electronic properties. Reaction with N -2,6-dimethylphenyl ( 1 ) and N -2,4,6-trimethylphenyl ( 2 ) ligands produced diamagnetic mono alkyl complexes; the structure of ( 1 )CoR was confirmed by X-ray diffraction. With the less shielding N -phenyl ( 3 ) and N -benzyl ( 4 ) ligands, 1 H NMR indicated formation of diamagnetic Co I alkyl species, but they were not stable enough to allow isolation. Fluorinated ligand 5 appears to be less reactive and−despite its supposedly stronger π-acceptor character−also does not lead to formation of a stable Co I alkyl complex. With PyBOX ligand 6, high-spin di alkyl complex ( 6 )CoR 2 was observed by 1 H NMR. Based on these observations and DFT calculations, a mechanism is proposed for formation of diiminepyridine Co I alkyls that involves formation of a high-spin κ 2 complex, spin flip to give a low-spin κ 3 complex, and irreversible loss of an alkyl radical.
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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) | 0.003 | 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".