Bifunctional Mn‐Fe Oxide Catalysts for Zn‐Air Battery Air Electrodes Fabricated Through Atomic Layer Deposition
Bibliographic record
Abstract
Abstract Zinc‐air batteries (ZABs) are one of many energy storage technologies that can help integrate renewable energy into the power grid. A key developmental goal for ZABs is replacing the precious metal catalysts at the air electrode with more abundant and inexpensive materials. In this work, a MnFexOy bifunctional catalyst is directly deposited on a ZAB air electrode using atomic layer deposition (ALD). With ALD, the atomic composition of the air electrode coating can be finely tuned based on catalytic activity. Characterization through electron microscopy, photoelectron spectroscopy and diffraction techniques indicate that the novel ALD film deposits as a nanocrystalline (Mn,Fe)3O4 cubic spinel. The mixed oxide catalyst outperforms its individual binary MnOx or FeOx constituents, operating at 52.5 % bifunctional efficiency at 20 mA cm−2. Moreover, the long term stability of the ALD catalyst is showcased by 600 h (1565 cycles) of ZAB cycling at 10 mA cm−2. The efficiency retention of the bifunctional transition metal oxide catalyst is superior to a precious metal benchmark of Pt−Ru−C, with 84.7 % efficiency retention after more than 1500 cycles versus only 66.2 % retention for the precious metal catalyst. The ALD technique enables deep penetration of catalyst material into the air electrode structure, improving the cycling behaviour.
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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".