Regioselective Multiboration and Hydroboration of Alkenes and Alkynes Enabled by a Platinum Single-Atom Catalyst
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Selective multiboration including di- and triboration and hydroboration of alkynes and alkenes face significant challenges in organic synthesis, including achieving high regioselectivity, functional group tolerance, and catalyst stability while requiring mild conditions to maintain reactivity. These transformations have been predominantly explored by using homogeneous catalysts. In this study, we report the scalable synthesis of heterogeneous platinum single-atom catalyst (Pt-SAC) supported on ultrathin nanosheets of graphitic carbon nitride via a rapid microwave-assisted method. The Pt-SAC enables 1,2-diboration of sterically hindered alkenes and 1,2,2-triboration of alkynes with B 2 pin 2 under mild conditions. For the diboration of styrene, the catalyst achieves 99% yield with 95% selectivity, a turnover number (TON) of 3711, and a turnover frequency (TOF) of 247 h –1 . The catalyst also promotes the regioselective hydroboration of alkenes and alkynes, yielding anti -Markovnikov alkylboranes and vinylboranes, respectively. Computational calculations reveal that the enhanced reactivity on the Pt-SAC catalyst arises from adsorption-induced weakening of key bonds (C=C and B–H), thereby significantly lowering the activation energy barriers. The Pt-SAC exhibits stability and recyclability, maintaining performance over at least eight consecutive runs without detectable Pt leaching. This study highlights the potential of Pt-SAC as a robust and versatile platform for organoboron transformations under mild conditions, with relevance to applications in pharmaceutical, agrochemical, and polymer synthesis.
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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 | 0.000 |
| 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) | 0.001 | 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".