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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".