Stabilizing Highly Active Ru Sites by Suppressing Lattice Oxygen Participation in Acidic Water Oxidation
Bibliographic record
Abstract
In hydrogen production, the anodic oxygen evolution reaction (OER) limits the energy conversion efficiency and also impacts stability in proton-exchange membrane water electrolyzers. Widely used Ir-based catalysts suffer from insufficient activity, while more active Ru-based catalysts tend to dissolve under OER conditions. This has been associated with the participation of lattice oxygen (lattice oxygen oxidation mechanism (LOM)), which may lead to the collapse of the crystal structure and accelerate the leaching of active Ru species, leading to low operating stability. Here we develop Sr–Ru–Ir ternary oxide electrocatalysts that achieve high OER activity and stability in acidic electrolyte. The catalysts achieve an overpotential of 190 mV at 10 mA cm –2 and the overpotential remains below 225 mV following 1,500 h of operation. X-ray absorption spectroscopy and 18 O isotope-labeled online mass spectroscopy studies reveal that the participation of lattice oxygen during OER was suppressed by interactions in the Ru–O–Ir local structure, offering a picture of how stability was improved. The electronic structure of active Ru sites was modulated by Sr and Ir, optimizing the binding energetics of OER oxo-intermediates.
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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 | 0.000 |
| 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".