Bioelectrocatalysis with a palladium membrane reactor
Bibliographic record
Abstract
Abstract Enzyme catalysis is used to generate approximately 50,000 tons of value-added chemical products per year. Nearly a quarter of this production requires a stoichiometric cofactor such as NAD + /NADH. Given that NADH is expensive, it would be beneficial to regenerate it in a way that does not interfere with the enzymatic reaction. Water electrolysis could provide the proton and electron equivalent necessary to electrocatalytically convert NAD + to NADH. However, this form of electrocatalytic NADH regeneration is challenged by the formation of inactive NAD 2 dimers, the use of high overpotentials or mediators, and the long-term electrochemical instability of the enzyme during electrolysis. Here, we show a means of overcoming these challenges by using a bioelectrocatalytic palladium membrane reactor for electrochemical NADH regeneration from NAD + . This achievement is possible because the membrane reactor regenerates NADH through reaction of hydride with NAD + in a compartment separated from the electrolysis compartment by a hydrogen-permselective Pd membrane. This separation of the enzymatic and electrolytic processes bypasses radical-induced NAD + degradation and enables the operator to optimize conditions for the enzymatic reaction independent of the water electrolysis. This architecture, which mechanistic studies reveal utilizes hydride sourced from water, provides an opportunity for enzyme catalysis to be driven by clean electricity where the major waste product is oxygen gas.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".