Stabilizing Double Perovskite for Effective Bifunctional Oxygen Electrocatalysis in Alkaline Conditions
Bibliographic record
Abstract
Oxygen electrocatalysis is at the heart of the emerging energy conversion and storage devices including reversible fuel cells and metal-air batteries. However, replacing the noble-metal-based oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalysts with affordable and robust alternatives remains challenging to date. Herein, we report a cation-ordered double perovskite oxide, i.e., PrBa 0.85 Ca 0.15 MnFeO 5+δ, with excellent stability and activity in both OER and ORR. The layered crystal structure provides ordered oxygen vacancy channels and a vast amount of surface oxygen defects, while the moderate amount of iron dopant keeps the B-site cations at high oxidation state with optimal e g fillings. Importantly, the DFT calculations along with the advanced TEM analysis verify that the incorporation of Ca at the A-site stabilizes the perovskite structure under potential bias. Such a bifunctional catalyst shows comparable, if not better, activity relative to the state-of-the-art perovskite oxides (e.g., Ba 0.5 Sr 0.5 Co 0.8 Fe 0.2 O 3−δ ) while demonstrating remarkably enhanced robustness. This work presents a rational approach of designing efficient, robust, and cost-effective perovskite oxide for oxygen electrocatalysis and sheds light on the influences of the crystallographic structure on the catalytic property.
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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".