Highly Active Graphene Nanosheets Prepared via Extremely Rapid Heating as Efficient Zinc-Air Battery Electrode Material
Bibliographic record
Abstract
A facile method has been developed based on extremely rapid heating (temperature ramp greater than 150°C sec−1) for the synthesis of graphene nanosheets with heterogeneously doped nitrogen atoms (ex-NG) in a one-step process. The nanosheets are uniquely characterized by large expansions and openings between the layers that facilitate in the diffusion of the electrolyte to perform highly active oxygen reduction reaction (ORR). Electron microscopy has verified a voile-like morphology of thermally reduced ex-NG, and X-ray photoelectron spectroscopy has confirmed successful ammonia treatment of nitrogen incorporation into the graphitic network. The ORR activity of the graphene nanosheets is evaluated using both half-cell and single-cell performance tests. The half-cell test is conducted by rotating disk electrode measurements where the nanosheets have showed very comparable ORR activity to that of state-of-the-art commercial Pt/C catalyst. A practical zinc-air battery have been utilized to test the single-cell performance of ex-NG1100, which have exhibited superior battery discharge voltages and reduced charge transfer resistance during the ORR compared to that of Pt/C. This outstanding catalytic activity of metal-free carbon-based graphene nanosheets is attributed to the opened structured attained by facile synthesis technique utilizing a rapid heating process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".