Mechanochemistry for sustainable and efficient dehydrogenation/hydrogenation
Bibliographic record
Abstract
Hydrogenation and dehydrogenation reactions are one of the pillars of the chemical industry, with applications from bulk chemicals to pharmaceuticals manufacturing. The ability to selectively add hydrogen across double and (or) triple bonds is key in the chemist’s toolbox and the enabling component in the development of sustainable processes. Traditional solution-based approaches to these reactions are tainted by significant consumption of energy and production of solvent waste. This review highlights the development and applications of recently emerged solvent-free approaches to conduct the hydrogenation of organic molecules using mechanochemistry, i.e., chemical transformations induced or sustained by mechanical force. In particular, we will show mechanochemical techniques such as ball-milling enabled catalytic or stoichiometric metal-mediated hydrogenation and dehydrogenation reactions that are simple, fast, and conducted under significantly milder conditions compared with traditional solution routes. Importantly, we highlight the current challenges and opportunities in this field, while also identifying exciting cases in which mechanochemical hydrogenation strategies lead to new, unique targets and reactivity.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".