Structural Insights into Multi‐Metal Spinel Oxide Nanoparticles for Boosting Oxygen Reduction Electrocatalysis
Bibliographic record
Abstract
Abstract Multi‐metal oxide (MMO) materials have significant potential to facilitate various demanding reactions by providing additional degrees of freedom in catalyst design. However, a fundamental understanding of the (electro)catalytic activity of MMOs is limited because of the intrinsic complexity of their multi‐element nature. Additional complexities arise when MMO catalysts have crystalline structures with two different metal site occupancies, such as the spinel structure, which makes it more challenging to investigate the origin of the (electro)catalytic activity of MMOs. Here, uniform‐sized multi‐metal spinel oxide nanoparticles composed of Mn, Co, and Fe as model MMO electrocatalysts are synthesized and the contributions of each element to the structural flexibility of the spinel oxides are systematically studied, which boosts the electrocatalytic oxygen reduction reaction (ORR) activity. Detailed crystal and electronic structure characterizations combined with electrochemical and computational studies reveal that the incorporation of Co not only increases the preferential octahedral site occupancy, but also modifies the electronic state of the ORR‐active Mn site to enhance the intrinsic ORR activity. As a result, nanoparticles of the optimized catalyst, Co 0.25 Mn 0.75 Fe 2.0 ‐MMO, exhibit a half‐wave potential of 0.904 V (versus RHE) and mass activity of 46.9 A g oxide −1 (at 0.9 V versus RHE) with promising stability.
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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 | 0.001 |
| 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) | 0.000 | 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".