Site-specific synergy by heteronuclear microenvironment atomic editing for oxygen reduction reaction
Bibliographic record
Abstract
Although iron-nitrogen-carbon catalysts are appealing for use in the oxygen reduction reaction, achieving high activity and a long lifetime remains a persistent challenge. This necessitates the precise modulation of the active sites’ microenvironment. Herein, we present a microenvironment atomic editing strategy for accessing heteronuclear triatomic Fe and Co sites of Fe1Co2N7O1 supported on a nitrogen-doped carbon matrix (Fe1Co2/NC). Its performance is boosted by the orbital hybridization between Fe and Co atoms, which alters the d band centers to push the activity (half-wave potential of 0.94 V in alkaline and 0.88 V in acid conditions) and stability boundaries to a high level. The optimized metal-adsorbate interactions and strengthened metal − N bonding in Fe1Co2N7O1 are responsible for the competitive activity and stability. Furthermore, rechargeable and flexible quasi-solid-state zinc-air batteries using this catalyst achieve high power density (282.7 mW cm−2 and 95.8 mW cm−2) and high operational stability, and are therefore more energy-efficient than commercial catalysts. Our findings underscore the importance of atomic editing for designing low-nuclearity catalysts. The development of advanced electrocatalysts with high efficiency and longevity is essential to alleviate the energy crisis. Here, the authors report an atomic editing strategy to access atomically dispersed, heteronuclear triatomic Fe and Co sites to endow improved activity and stability.
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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) | 0.002 | 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".