Ruthenium‐Functionalized Hierarchical Carbon Nanocages as Efficient Catalysts for Li‐O<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract Developing an efficient cathode is essential to obtain high rate capability and rechargeable lithium oxygen (Li‐O2) batteries. Herein, ruthenium (Ru)‐functionalized hierarchical carbon nanocages (hCNCs) are synthesized and employed as a cathode catalyst for Li‐O2 batteries. The as‐prepared cathode exhibits high discharge capacity, low charge potential (8135 mA h g−1 with 3.85 V charge potential at a current density of 0.08 mA cm−2), outstanding rate capability (3416 mA h g−1 at a current density of 0.48 mA cm−2) and good stability up to 78 cycles at a limited capacity of 500 mA h g−1. Such excellent battery performance is ascribed to the synergistic effect of the interconnected hierarchically porous structure of hCNCs, which can facilitate effective electrolyte immersion and efficient Li+/O2 mass transport, and the high catalytic activity of Ru nanoparticles.
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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".