Bibliographic record
Abstract
A long-standing scientific challenge in the field of alcohol deoxygenation has been direct catalytic sp(3) C-O defunctionalization with high selectivity and efficiency, in the presence of other functionalities, such as free hydroxyl groups and amines widely present in biological molecules. Previously, the selectivity issue had been only addressed by classic multistep deoxygenation strategies with stoichiometric reagents. Herein, we propose a catalytic late-transition-metal-catalyzed redox design, on the basis of dehydrogenation/Wolff-Kishner (WK) reduction, to simultaneously tackle the challenges regarding step economy and selectivity. The early development of our hypothesis focuses on an iridium-catalyzed process efficient mainly with activated alcohols, which dictates harsh reaction conditions and thus limits its synthetic utility. Later, a significant advancement has been made on aliphatic primary alcohol deoxygenation by employing a ruthenium complex, with good functional group tolerance and exclusive selectivity under practical reaction conditions. Its synthetic utility is further illustrated by excellent efficiency as well as complete chemo- and regio-selectivity in both simple and complex molecular settings. Mechanistic discussion is also included with experimental supports. Overall, our current method successfully addresses the aforementioned challenges in the pertinent field, providing a practical redox-based approach to the direct sp(3) C-O defunctionalization of aliphatic primary alcohols.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".