High Voltage Stability and Characterization of P2‐Na<sub>0.66</sub>Mn<sub>1‐<i>y</i></sub>Mg<sub><i>y</i></sub>O<sub>2</sub> Cathode for Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract The development of sodium‐ion batteries is currently limited by the availability of high‐energy and low‐cost cathode materials. The energy density of cathodes can be maximized by expanding the voltage range for cycling, but this often leads to severe capacity degradation. Cathodes that demonstrate good long‐term cyclability at high voltage cut‐offs (>4.3 V vs Na/Na + ) are scarce in the literature. In this work, layered P2‐Na 0.66 Mn 1‐ y Mg y O 2 was synthesized by using a modified Pechini method at various compositions ( y =0, 0.05, 0.1) and characterized after extended cycling between 2–4.5, 4.6, and 4.7 V vs. Na/Na + . Na 0.66 Mn 0.95 Mg 0.05 O 2 displayed a similar initial discharge capacity to Na 0.66 MnO 2 with significant improvements in cycle retention. It was most promising when cycled between 2 and 4.5 V, retaining 140 mAh g −1 (82 % retention) and 116 mAh g −1 (68 % retention) after 50 and 100 cycles, respectively, at low current (40 mA g −1 ). A higher Mg dopant quantity led to improvements in cyclability and rate performance albeit with lower initial discharge capacity. Electrochemical and physical (ex situ XRD) characterizations were used to delineate the role of high‐voltage phase transitions, SEI layer formation, electrolyte solvent insertion into sodium slabs, and active material degradation/dissolution toward capacity loss.
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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".