Mechanism of Anion-Catalyzed C–H Silylation Using TMSCF<sub>3</sub>: Kinetically-Controlled CF<sub>3</sub>-Anionoid Partitioning As a Key Parameter
Bibliographic record
Abstract
The mechanism of anion-catalyzed C–H silylation by R3SiCF3 reagents has been investigated using homogeneous TBAT-initiation, in situ and stopped-flow 19F NMR spectroscopy 2H-KIE, LFER, deuterium-labeled crossover, structure-selectivity quantitation (TMSCF3/TESCF3), carbene trapping, and DFT-calculations. Analysis of the kinetics of reactions of 1,3-difluorobenzenes (2), and the generation of ArSiMe3 and Me3SiF as a function of the concentration of [2], [TMSCF3], and [TBAT], show that a CF3-anionoid is the active intermediate. The CF3-anionoid is reversibly released from siliconate [(CF3)2SiMe3]− and undergoes partitioning through rate-limiting arene deprotonation (1H/2H KIE 9.5) to generate ArSiMe3 (via a transient aryl anionoid) and fluoroform (CF3H), in competition with F-anion transfer to TMSCF3 to generate CF2 and TMSF. The [2]/[TMSCF3] concentration ratio directly and proportionally controls the kinetics of the partition, in favor of C–H deprotonation. Higher concentrations of TBAT and lower concentrations of TMSCF3 lead to faster rates of ArSiMe3 generation. Use of the homologous TESCF3 reagent leads to faster rates of anion catalysis and an increased selectivity toward C–H deprotonation. Perfluoroalkenes, generated in situ from CF2, capture the CF3-anionoid leading to progressive inhibition of the anion-catalysis. Inhibition is suppressed by using a styrene additive to trap the CF2 and the efficiency of the process enhanced by slow-addition of TMSCF3 (1) to maintain a high concentration ratio [2]/[1].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".