Metal‐Organic‐Framework‐Derived Co Nanoparticles Deposited on N‐Doped Bimodal Mesoporous Carbon Nanorods as Efficient Bifunctional Catalysts for Rechargeable Zinc−Air Batteries
Bibliographic record
Abstract
Abstract Electrically rechargeable zinc−air batteries (ZnABs) have received increasing attention as promising energy storage devices, owing to their high theoretical energy density and environmental friendliness. However, it remains a great challenge to develop highly efficient bifunctional catalysts for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Herein, we design and prepare a highly active bifunctional catalyst for ZnABs by using a cobalt metal‐organic framework (Co‐MOF) as the precursor. The catalyst has a desirable nanostructure composed of cobalt nanoparticles deposited on N‐doped bimodal mesoporous carbon nanorods (Co@N‐CNR). This hybrid structure exhibits a higher catalytic activity (a maximum power density of 63 mW cm−2) and cycling stability compared to commercial Pt/C+Ir/C integrated in ZnABs. The high catalytic performance could be attributed to the unique nanostructure composed of Co@N‐CNR, which incorporates the advantageous features of cobalt nanoparticles, mesoporous materials, and N‐CNR towards OER and ORR. The reported advancement provides a new and efficient strategy for the development of rechargeable ZnABs.
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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".