Co–N Decorated Hierarchically Porous Graphene Aerogel for Efficient Oxygen Reduction Reaction in Acid
Bibliographic record
Abstract
Nitrogen-functionalized graphene materials have been demonstrated as promising electrocatalyst for the oxygen reduction reaction (ORR), owning to their respectable activity and excellent stability in alkaline electrolyte. However, they exhibit unacceptable catalytic activity in acid medium. Here, a hierarchically porous Co-N functionalized graphene aerogel is prepared as an efficient catalyst for the ORR in acid electrolyte. In the preparation process, polyaniline (PANI) is introduced as a pore-forming agent to aid in the self-assembly of graphene species into a porous aerogel networks, and a nitrogen precursor to induce in situ nitrogen doping. Therefore, a Co-N decorated graphene aerogel framework with a large surface area (485 m(2) g(-1)) and an abundance of meso/macropores is effectively formed after heat treatment. Such highly desired structures can not only expose sufficient active sites for the ORR but also guarantee the fast mass transfer in the catalytic process, which provides significant catalytic activity with positive onset and half wave potentials, low hydrogen peroxide yield, high resistance to methanol crossover, and remarkable stability that is comparable to commercial Pt/C in acid medium.
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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) | 0.000 | 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 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".