Electrolysis Can Be Used to Resolve Hydrogenation Pathways at Palladium Surfaces in a Membrane Reactor
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide For common hydrogenation chemistries that occur at high temperatures (where H 2 is adsorbed and activated at the same surface which the substrate must also adsorb for reaction), there is often little consensus on how the reactions (e.g., hydro(deoxy)genation) actually occur. We demonstrate here that an electrocatalytic palladium membrane reactor (ePMR) can be used to study hydrogenation reaction mechanisms at ambient temperatures, where the catalyst does not necessarily undergo structural reorganization. The ePMR uses electrolysis and a hydrogen-selective palladium membrane to deliver reactive hydrogen to a catalyst surface in an adjacent compartment for reaction with an organic substrate. This process forms the requisite metal-hydride surface for hydrogenation chemistry, but at ambient temperature and pressure, and without a H 2 source. We demonstrate the utility of this analytical tool by studying the hydrogenation of benzaldehyde at palladium nanocubes with dimensions of 13–24 nm. This experimental design enabled us to resolve that the alcohol product forms at the facial sites, whereas the hydrodeoxygenation step occurs at edge sites. These observations enabled us to develop the first site-specific definition of how a carbonyl species undergoes hydro(deoxy)genation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".