Synergistic Binary Fe–Co Nanocluster Supported on Defective Tungsten Oxide as Efficient Oxygen Reduction Electrocatalyst in Zinc‐Air Battery
Bibliographic record
Abstract
Abstract Rational design of metal oxide supported non‐precious metals is essential for the development of stable and high‐efficiency oxygen reduction reaction (ORR) electrocatalysts. Here, an efficient ORR catalyst consisting of binary Fe/Co nanoclusters supported by defective tungsten oxide and embedded N‐doped carbon layer (NC) with a 3D ordered macroporous architecture (3DOM Fe/Co@NC‐WO 2− x ) is developed. The oxygen deficient 3DOM WO 2− x not only serves as a porous and stable support, but also enhances the conductivity and ensures good dispersion of the binary Fe/Co nanocluster, benefiting its ORR catalytic activity. Theoretical calculation shows that there exists a synergistic effect of electron transfer from Fe to Co in the supported binary Fe/Co cluster, promoting the ORR reaction energetics. Accordingly, the 3DOM Fe/Co@NC‐WO 2− x catalyst exhibits excellent ORR activity in alkaline medium with a half wave potential ( E 1/2 ) of 0.87 V higher than that of Pt/C (0.85 V). The zinc–air batteries assembled by 3DOM Fe/Co@NC‐WO 2− x cathode deliver a higher power density and specific capacity than that of Pt/C. A new strategy of combining synergistic binary‐metal nanoclusters and conductive metal oxide support design is provided here to develop efficient and durable ORR electrocatalyst.
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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) | 0.000 | 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 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".