Mechanistic Insight into Hydroboration of Imines from Combined Computational and Experimental Studies
Bibliographic record
Abstract
Abstract Boron Lewis acid‐catalyzed and catalyst‐free hydroboration reactions of imines are attractive due to the mild reaction conditions. In this work, the mechanistic details of the hydroboration reactions of two different kinds of imines with pinacolborane (HBpin) are investigated by combining density functional theory calculations and some experimental studies. For the hydroboration reaction of N‐(α‐methylbenzylidene)aniline catalyzed by tris[3,5‐bis(trifluoromethyl)phenyl]borane (BArF3), our calculations show that the reaction proceeds through a boron Lewis acid‐promoted hydride transfer mechanism rather than the classical Lewis acid activation mechanism. For the catalyst‐ and solvent‐free hydroboration reaction of imine, N‐benzylideneaniline, our calculations and experimental studies indicate that this reaction is difficult to occur under the reaction conditions reported previously. With a combination of computational and experimental studies, we have established that the commercially available BH3 ⋅ SMe2 can serve as an efficient catalyst for the hydroboration reactions of N‐benzylideneaniline and similar imines. The hydroboration reactions catalyzed by BH3 ⋅ SMe2 are most likely to proceed through a hydroboration/B−H/B−N σ‐bond metathesis pathway, which is very different from that of the reaction catalyzed by BArF3.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".