Trapping Hydrogen: Confined Catalysis for Improved Alcohol Amination Selectivity
Bibliographic record
Abstract
Abstract Confined chemistry is a powerful tool in catalysis. In this study, we report hierarchical structures with controlled morphology able to trap labile intermediates and improve a catalytic cascade reaction. We used alcohol amination via hydrogen borrowing as model, a process that gives substituted amines from alcohols and does not require the addition of hydrogen to reduce the imines or the use of coupling agents. A common problem however in those systems is the loss of the borrowed hydrogen atoms, leading to stagnation of the product at the imine stage. To this end, we encapsulated Al2O3/Ru(OH)x nanocatalysts inside mesoporous silica in a yolk‐shell architecture and were able to trap the hydrogens to increase the amine yield from 12% to 82%, with a three‐fold increase in selectivity without the need of any additive. We found the presence of mesopores in the silica shells to be essential to enable access to the catalytic sites and the yolk‐shell gap size to be the key parameter influencing the reactivity of the catalytic system. To the best of our knowledge, this is the first report of a confined hydrogen borrowing reaction, an approach that can be extended to the other types of cascade reactions that produce labile intermediates.
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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".