On the Cycling Performance of Na‐O<sub>2</sub> Cells: Revealing the Impact of the Superoxide Crossover toward the Metallic Na Electrode
Bibliographic record
Abstract
Abstract Na‐O2 batteries have attracted extensive attention as promising candidates for large‐scale energy storage due to their ultrahigh theoretical energy density. However, the poor cycling performance of Na‐O2 batteries is one of the major challenges facing its future development. A novel Na‐O2 battery using electrically connected carbon paper with Na metal as a protected anode is presented in this study. The O2− crossover from the cathode to anode partially contributes to the limited Coulombic efficiency, as well as the Na corrosion during the cycling process. For the cells with protected Na, the carbon paper maintains a pseudo‐equal potential with the Na metal and works as an artificial protective layer to suppress the detrimental side reactions caused by O2− and O2 crossover toward the Na electrode. Furthermore, the short‐circuiting issue caused by Na dendrite growth also can be completely resolved. Consequently, the Na‐O2 cells with protected Na exhibit two times higher discharge capacity and cycling stability compared with the cells using bare Na. These results indicate the crucial role of the Na anode in determining the overall cell performance and a rational design of anode can dramatically contribute to develop advanced Na‐O2 batteries with longer lifespans and better cycling performance.
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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) | 0.002 | 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".