Cu Nanoclusters/FeN<sub>4</sub> Amorphous Composites with Dual Active Sites in N-Doped Graphene for High-Performance Zn–Air Batteries
Bibliographic record
Abstract
Exploring inexpensive and earth-abundant transition metal–nitrogen-based carbon (MNC) catalysts to substitute the scarce and costly Pt-based electrocatalysts for the oxygen reduction reaction (ORR) is quite anticipated in metal–air batteries (MABs). Here, we demonstrate a facile vacuum-annealing method to synthesize Cu nanoclusters/FeN 4 amorphous composites embedded in N-doped graphene (Cu/Fe-NG). This approach avoids the long-term pyrolysis procedure and the use of an inert atmosphere in the conventional procedure for fabricating MNC catalysts. Interestingly, we discovered that the amorphous structure of Cu/FeN 4 composites can provide high-activity bimetallic M–N x sites (M = Cu, Fe), because of which the Cu/FeN 4 composites exhibit boosted electrocatalytic activity with a positive half-wave potential of 0.88 V (vs RHE), long-term durability, and low hydrogen peroxide for the ORR. The origin of this enhancement was assigned to the concomitance of Fe–N 4 and Cu–N x moieties in Cu/Fe-NG, favoring adsorption and activation of the O 2 molecule as suggested by X-ray absorption fine structure (XAFS) analyses and density functional theory (DFT) calculations. Moreover, examinations of Cu/Fe-NG in both liquid and quasi-solid-state Zn–air batteries (ZABs) can exhibit remarkable performances. This work may offer facile fabrication of enhanced performance MNC catalysts as well as a profound insight into the use of amorphous materials in the ORR and ZABs.
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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".