Engineering the Electronic Structure of Single‐Atom Iron Sites with Boosted Oxygen Bifunctional Activity for Zinc–Air Batteries
Bibliographic record
Abstract
Abstract Rechargeable zinc–air batteries typically require efficient, durable, and inexpensive bifunctional electrocatalysts to support oxygen reduction/evolution reactions (ORR/OER). However, sluggish kinetics and mass transportation challenges must be addressed if the performance of these catalysts is to be enhanced. Herein, a strategy to fabricate a catalyst comprising atomically dispersed iron atoms supported on a mesoporous nitrogen‐doped carbon support (Fe SAs/NC) with accessible metal sites and optimized electronic metal–support interactions is developed. Both the experimental results and theoretical calculations reveal that the engineered electronic structures of the metal active sites can regulate the charge distribution of Fe centers to optimize the adsorption/desorption of oxygenated intermediates. The Fe SAs/NC containing Fe1N4O1 sites achieves remarkable ORR activity over the entire pH range, with half‐wave potentials of 0.93, 0.83, and 0.75 V (vs reversible hydrogen electrode) in alkaline, acidic, and neutral electrolytes, respectively. In addition, it demonstrates a promising low overpotential of 320 mV at 10 mA cm−2 for OER in alkaline conditions. The zinc–air battery assembled with Fe SAs/NC exhibits superior performance than that of Pt/C+RuO2 counterpart in terms of peak power density, specific capacity, and cycling stability. These findings demonstrate the importance of the electronic structure engineering of metal sites in directing catalytic activity.
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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".