Metal–Organic Framework-Derived Reduced Graphene Oxide-Supported ZnO/ZnCo<sub>2</sub>O<sub>4</sub>/C Hollow Nanocages as Cathode Catalysts for Aluminum–O<sub>2</sub> Batteries
Bibliographic record
Abstract
Aluminum–air battery is a promising candidate for large-scale energy applications because of its low cost and high energy density. Remarkably, tremendous efforts have been concentrated on developing efficient and stable cathode electrocatalysts toward the oxygen reduction reaction. In this work, a hydrothermal-calcination approach was utilized to prepare novel reduced graphene oxide (rGO)-supported hollow ZnO/ZnCo 2 O 4 nanoparticle-embedded carbon nanocages (ZnO/ZnCo 2 O 4 /C@rGO) using a zeolitic imidazolate framework (ZIF-67)/graphene oxide/zinc nitrate composite as the precursor. The ZnO/ZnCo 2 O 4 /C@rGO hybrid exhibits remarkable electrocatalytic performance for oxygen reduction reaction under alkaline conditions and superior stability and methanol tolerance to those of the commercial Pt/C catalyst. Furthermore, novel and simple Al–air coin cells were first fabricated using the hybrid materials as cathode catalysts under ambient air conditions to further investigate their catalytic performance. The coin cell with the ZnO/ZnCo 2 O 4 /C@rGO cathode catalyst displays a higher open circuit voltage and discharge voltage and more sluggish potential drop than those of the cell with the ZnO/ZnCo 2 O 4 /C cathode catalyst, which confirms that rGO can enhance the electrocatalytic activity and stability of the catalyst system. The excellent electrocatalytic performance of the ZnO/ZnCo 2 O 4 /C@rGO hybrid is attributed to the prominent conductivity and high specific surface area resulting from rGO, the more accessible catalytic active sites induced by the unique porous hollow nanocage structure, and synergic covalent coupling between rGO sheets and ZnO/ZnCo 2 O 4 /C nanocages.
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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".