Electronic and Steric Parameters of 76 N-Heterocyclic Carbenes in Ni(CO)<sub>3</sub>(NHC)
Bibliographic record
Abstract
We report electron-donor and steric properties of a diverse group of representative N-heterocyclic carbene (NHC) ligands quantified with the help of DFT calculations. This study afforded the conventional TEP data (Tolman electronic parameter = ν CO (A 1 ) of Ni(CO) 3 (NHC)), which allowed ranking 76 NHC ligands in order of increasing donor power. The TEP data reveal several general trends concerning the influence of NHC ring size, substitution, and annulation. The calculations also provided reaction enthalpies for CO elimination from the Ni(CO) 3 (NHC) complexes and formation of the 16-electron Ni(CO) 2 (NHC) species. This reaction is largely under steric control, which allowed defining a new steric descriptor for NHC ligands, r (“repulsiveness”) = 10 × (7.568 − 0.003172TEP − 0.0446Δ H ), ranging from 0.0 for the smallest (Im N H 2 ) to 8.0 for the most repulsive carbene (Im N Ad 2 ) of this work. Ni(CO) 3 L and Os(H 2 )Cl 2 (CO)L 2 complexes eliminate CO and H 2, respectively, more readily with L = NHC vs PR 3 ligands. Apparently, even relatively small NHC ligands are very sterically demanding, and this property of N-heterocyclic carbenes may play a major role in coordination chemistry and catalysis.
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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 | 0.000 |
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".