The Excellence of Both Worlds: Developing Effective Double Perovskite Oxide Catalyst of Oxygen Reduction Reaction for Room and Elevated Temperature Applications
Bibliographic record
Abstract
The efficiencies of a number of electrochemical devices (e.g., fuel cells and metal‐air batteries) are mainly governed by the kinetics of the oxygen reduction reaction (ORR). Among all the good ORR catalysts, the partially substituted double perovskite oxide (AA′B 2 O 5+δ ) has the unique layered structure, providing a great flexibility regarding the optimization of its electronic structures and physicochemical properties. Here, it is demonstrated that the double perovskite oxide, i.e., NdBa 0.75 Ca 0.25 Co 1.5 Fe 0.5 O 5+δ , is a good ORR catalyst at both room and elevated temperatures. Under ambient condition, its half‐wave potential of ORR in alkaline media is as low as 0.74 V versus RHE; at 650 °C, the cathodic polarization resistance is merely 0.0276 Ω cm 2 according to a symmetric cell measurement, whereas the solid oxide fuel cells using this cathode exhibit a maximum power density of 1982 mW cm −2 . From various materials characterizations, it is hypothesized that its excellent ORR activity is strongly correlated with the crystallographic, electronic, and defect structures of the materials.
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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 | 0.000 |
| 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".