Mixed Electron–Proton Conductors Enable Spatial Separation of Bond Activation and Charge Transfer in Electrocatalysis
Bibliographic record
Abstract
Electrochemical energy conversion requires electrodes that can simultaneously facilitate substrate bond activation and electron–proton charge transfer. Traditional electrodes co-localize both functions to a single solid|liquid interface even though each process is typically favored in a disparate reaction environment. Herein, we establish a strategy for spatially separating bond activation and charge transfer by exploiting mixed electron–proton conduction (MEPC) in an oxide membrane. Specifically, we interpose a MEPC WO x membrane between a Pt catalyst and aqueous electrolyte and show that this composite electrode is active for the hydrogen oxidation reaction (HOR). Consistent with H 2 activation occurring at the gas|solid interface, the composite electrode displays HOR current densities over 8-fold larger than the diffusion-limited rate of HOR catalysis at a singular Pt|solution interface. The segregation of bond activation and charge separation steps also confers excellent tolerance to poisons and impurities introduced to the electrolyte. Mechanistic studies establish that H 2 activation at the Pt|gas interface is coupled to the electron–proton charge separation at the WO x |solution interface via rapid H-diffusion in the bulk of the WO x . Consequently, the rate of HOR is principally controlled by the rate of H-spillover at the Pt|WO x boundary. Our results establish MEPC membrane electrodes as a platform for spatially separating the critical bond activation and charge transfer steps of electrocatalysis.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".