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/FeN4 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/FeN4 composites can provide high-activity bimetallic M–Nx sites (M = Cu, Fe), because of which the Cu/FeN4 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–N4 and Cu–Nx moieties in Cu/Fe-NG, favoring adsorption and activation of the O2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".