Bias‐Induced Ga−O‐Ir Interface Breaks the Limits of Adsorption‐Energy Scaling Relationships for High‐Performing Proton Exchange Membrane Electrolyzers
Bibliographic record
Abstract
Abstract Rationally manipulating the in situ formed catalytically active surface of catalysts remains a significant challenge for achieving highly efficient water electrolysis. Herein, we present a bias‐induced activation strategy to modulate in situ Ga leaching and trigger the dynamic surface restructuring of lamellar Ir@Ga 2 O 3 for the electrochemical oxygen evolution reaction. The in situ reconstructed Ga−O−Ir interface sustains high water oxidation rates at oxygen evolution reaction (OER) overpotentials. We found that OER at the Ga−O−Ir interface follows a bi‐nuclear adsorbate evolution mechanism with unsaturated IrO x as the active sites, while GaO x atoms play an indirect role in promoting water dissociation to form OH* and transferring OH* to Ir sites. This breaks the scaling relationship of the adsorption energies between OH* and OOH*, significantly lowering the energy barrier of the rate‐limiting step and greatly increasing reactivity. The Ir@Ga 2 O 3 catalyst achieves lower overpotentials, a current density of 2 A cm −2 at 1.76 V, and stable operation up to 1 A cm −2 in scalable proton exchange membrane water electrolyzer (PEMWE) at 1.63 V, maintaining stable operation at 1 A cm −2 over 1000 hours with a degradation rate of 11.5 μV h −1 . This work prompted us to jointly address substrate–catalyst interactions and catalyst reconstruction, an underexplored path, to improve activity and stability in Ir PEMWE anodes.
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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 | 0.000 |
| 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".