Preferentially Engineering FeN<sub>4</sub> Edge Sites onto Graphitic Nanosheets for Highly Active and Durable Oxygen Electrocatalysis in Rechargeable Zn–Air Batteries
Bibliographic record
Abstract
Abstract Single‐atom FeN4 sites at the edges of carbon substrates are considered more active for oxygen electrocatalysis than those in plane; however, the conventional high‐temperature pyrolysis process does not allow for precisely engineering the location of the active site down to atomic level. Enlightened by theoretical prediction, herein, a self‐sacrificed templating approach is developed to obtain edge‐enriched FeN4 sites integrated in the highly graphitic nanosheet architecture. The in situ formed Fe clusters are intentionally introduced to catalyze the growth of graphitic carbon, induce porous structure formation, and most importantly, facilitate the preferential anchoring of FeN4 to its close approximation. Due to these attributes, the as‐resulted catalyst (denoted as Fe/N‐G‐SAC) demonstrates unprecedented catalytic activity and stability for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) by showing an impressive half‐wave potential of 0.89 V for the ORR and a small overpotential of 370 mV at 10 mA cm−2 for the OER. Moreover, the Fe/N‐G‐SAC cathode displays encouraging performance in a rechargeable Zn–air battery prototype with a low charge–discharge voltage gap of 0.78 V and long‐term cyclability for over 240 cycles, outperforming the noble metal benchmarks.
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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".