Catalyst-Controlled Nitrene Transfer by Tuning Metal:Ligand Ratios: Insight into the Mechanisms of Chemoselectivity
Bibliographic record
Abstract
Catalyst-controlled, selective nitrene transfer is often challenging when both C–H and C═C bonds are present in a substrate. Interestingly, a simple change in the Ag(I):L ratio (L = bidentate N,N-donor ligand) enables tunable, chemoselective nitrene transfer that favors either C═C bond aziridination using an ∼1:1 Ag:L ratio (AgLOTf) or insertion into a C–H bond when the Ag:L ratio in the catalyst is 1:2 (AgL 2 OTf). In this paper, mechanistic studies, coupled with kinetic profiling of the entire reaction course, are employed to examine the reasons for this unusual behavior. Steady-state kinetics were found to be similar for both AgLOTf and AgL 2 OTf; both complexes yield electronically similar reactive intermediates that engage in nitrene transfer involving formation of a short-lived radical intermediate and barrierless radical recombination. Taken together, experimental and computational studies point to two effects that control tunable chemoselectivity: suppression of aziridination as the steric congestion around the silver center is increased in AgL 2 OTf and a decrease in the rate of C–H insertion with AgLOTf in comparison to AgL 2 OTf. The observation that the sterics of Ag catalysts can be varied, with minor effects on the electronic features of the putative nitrene, has important implications for the development of other silver catalysts that enable tunable, site-selective C–H bond aminations.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".