Manganese-Catalyzed Hydroboration of Terminal Olefins and Metal-Dependent Selectivity in Internal Olefin Isomerization–Hydroboration
Bibliographic record
Abstract
In the past decade, the use of earth-abundant metals in homogeneous catalysis has flourished. In particular, metals such as cobalt and iron have been used extensively in reductive transformations including hydrogenation, hydroboration, and hydrosilylation. Manganese, on the other hand, has been considerably less explored in these reductive transformations. Here, we report a well-defined manganese complex, [Mn(iPrBDI)(OTf)2] (2a; BDI = bipyridinediimine), that is an active precatalyst in the hydroboration of a variety of electronically differentiated alkenes (>20 examples). The hydroboration is specifically selective for terminal alkenes and occurs with exclusive anti-Markovnikov selectivity. In contrast, when using the analogous cobalt complex [Co(iPrBDI)(OTf)2] (3a), internal alkenes are hydroborated efficiently, where a sequence of isomerization steps ultimately leads to their hydroboration. The contrasting terminal versus internal alkene selectivity for manganese and cobalt was investigated computationally and is further discussed in the herein-reported study.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".