3D hollow sphere Co3O4/MnO2-CNTs: Its high-performance bi-functional cathode catalysis and application in rechargeable zinc-air battery
Bibliographic record
Abstract
There has been a continuous need for high active, excellently durable and low-cost electrocatalysts for rechargeable zinc-air batteries. Among many low-cost metal based candidates, transition metal oxides with the CNTs composite have gained increasing attention. In this paper, the 3-D hollow sphere MnO2 nanotube-supported Co3O4 nanoparticles and its carbon nanotubes hybrid material (Co3O4/MnO2-CNTs) have been synthesized via a simple co-precipitation method combined with post-heat treatment. The morphology and composition of the catalysts are thoroughly analyzed through SEM, TEM, TEM-mapping, XRD, EDX and XPS. In comparison with the commercial 20% Pt/C, Co3O4/MnO2, bare MnO2 nanotubes and CNTs, the hybrid Co3O4/MnO2-CNTs-350 exhibits perfect bi-functional catalytic activity toward oxygen reduction reaction and oxygen evolution reaction under alkaline condition (0.1 M KOH). Therefore, high cell performances are achieved which result in an appropriate open circuit voltage (∼1.47 V), a high discharge peak power density (340 mW cm−2) and a large specific capacity (775 mAh g−1 at 10 mA cm−2) for the primary Zn-air battery, a small charge–discharge voltage gap and a high cycle-life (504 cycles at 10 mA cm−2 with 10 min per cycle) for the rechargeable Zn-air battery. In particular, the simple synthesis method is suitable for a large-scale production of this bifunctional material due to a green, cost effective and readily available process.
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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".