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 R 3 SiCF 3 reagents has been investigated using homogeneous TBAT-initiation, in situ and stopped-flow 19 F NMR spectroscopy 2 H-KIE, LFER, deuterium-labeled crossover, structure-selectivity quantitation (TMSCF 3 /TESCF 3 ), carbene trapping, and DFT-calculations. Analysis of the kinetics of reactions of 1,3-difluorobenzenes ( 2 ), and the generation of ArSiMe 3 and Me 3 SiF as a function of the concentration of [ 2 ], [TMSCF 3 ], and [TBAT], show that a CF 3 -anionoid is the active intermediate. The CF 3 -anionoid is reversibly released from siliconate [(CF 3 ) 2 SiMe 3 ] − and undergoes partitioning through rate-limiting arene deprotonation ( 1 H/ 2 H KIE 9.5) to generate ArSiMe 3 (via a transient aryl anionoid) and fluoroform (CF 3 H), in competition with F-anion transfer to TMSCF 3 to generate CF 2 and TMSF. The [ 2 ]/[TMSCF 3 ] 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 TMSCF 3 lead to faster rates of ArSiMe 3 generation. Use of the homologous TESCF 3 reagent leads to faster rates of anion catalysis and an increased selectivity toward C–H deprotonation. Perfluoroalkenes, generated in situ from CF 2, capture the CF 3 -anionoid leading to progressive inhibition of the anion-catalysis. Inhibition is suppressed by using a styrene additive to trap the CF 2 and the efficiency of the process enhanced by slow-addition of TMSCF 3 ( 1 ) to maintain a high concentration ratio [ 2 ]/[ 1 ].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".