How to Control the Discharge Products in Na–O<sub>2</sub> Cells: Direct Evidence toward the Role of Functional Groups at the Air Electrode Surface
Bibliographic record
Abstract
Sodium–oxygen batteries have received a significant amount of research attention as a low-overpotential alternative to lithium–oxygen. However, the critical factors governing the composition and morphology of the discharge products in Na–O 2 cells are not thoroughly understood. Here we show that oxygen containing functional groups at the air electrode surface have a substantial role in the electrochemical reaction mechanisms in Na–O 2 cells. Our results show that the presence of functional groups at the air–electrode surface conducts the growth mechanism of discharge products toward a surface-mediated mechanism, forming a conformal film of products at the electrode surface. In addition, oxygen reduction reaction at hydrophilic surfaces more likely passes through a peroxide pathway, which results in the formation of peroxide-based discharge products. Moreover, in-line X-ray diffraction combined with solid state 23 Na NMR results indicate the instability of discharge products against carbonaceous electrodes. The findings of this study help to explain the inconsistency among various reports on composition and morphology of the discharge products in Na–O 2 cells and allow the precise control over the discharge products.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".