Bibliographic record
Abstract
Among the most common ligands found on transition metal catalysts are halide ions. Of the commercially available catalysts or pre-catalysts, most are halo-metal complexes. In recent years, manipulation of this metal-halide functionality has revealed that this can be used as a highly valuable method of tuning the reactivity of the complex. Variation of the halide ligand will usually not alter the nature of the system to the extent that it becomes unreactive but will impart sufficiently large changes that differences in reactivity or selectivity occur. These differences are a product of the steric and electronic properties of the halide ligand which has the ability to donate electron density to the metal occurs in a predictable manner. Despite the common perception in asymmetric catalysis that halide ligands are of secondary importance compared to chiral ligands, halide ligands have been found to exert dramatic effects on the enantioselectivity of asymmetric transformations. While the mechanism of action is known for relatively few of the cases, many intriguing and potentially synthetically useful trends are apparent. This review discusses the physical properties of the halides and their effects on stoichiometric and catalytic transition metal processes. The metal-halide moiety thus emerges as a tunable functionality on the transition metal catalyst that can be used in the development of new catalytic systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".