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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".