C-N triple bond cleavage via trans-membrane hydrogenation
Bibliographic record
Abstract
The renewable energy-driven valorization of excess feedstocks into commodity chemicals and societally useful products constitutes a longstanding push in energy and sustainability research. To this end, this work pushes to expand the scope of green electrosynthesis by innovating a new approach to convert acetonitrile, industrially generated in excess and burned off, to in-demand ammonia and acetaldehyde products. Success here was enabled through the use of a Pd-membrane based reactor which abstracted hydrogen atoms from water, which subsequently diffused through to a separate organic compartment in which they carried out the hydrogenation reaction. In this geometry, the reaction proceeded at 5.2 mA/cm2 partial current density and 60% Faradaic efficiency towards ammonia generation. Further, the transmembrane hydrogenation approach gave rise to an onset potential of 0.2VAg/AgCl, surpassing previous state-of-the-art systems by 0.7V. A customized infrared spectroelectrochemcal setup was built up to probe the mechanism of the reaction, which was shown to proceed through an imine hydrolysis like pathway, with the hydrogenation of the NHx species that remained being the rate-limiting steps in the process. This work establishes a new route in electrochemical nitrile hydrogenation and general opens up promising avenues in green electrosynthesis.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".