Impact of Calcium on Air Stability of Na[Ni<sub>1/3</sub>Fe<sub>1/3</sub>Mn<sub>1/3</sub>]O<sub>2</sub> Positive Electrode Material for Sodium-ion Batteries
Bibliographic record
Abstract
O3-type Na[Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 is a promising positive electrode material for sodium-ion batteries. However, it suffers from structural degradation accompanied by surface-impurity growth during ambient storage and processing. In this study, we synthesized Na 1–2y Ca y [Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 with y = 0 and 0.02, and studied their structural stability towards ambient exposure. Na 0.96 Ca 0.02 [Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 demonstrated excellent air stability by retaining 0.93 of lattice Na in the original O3 structure after 6 days of ambient storage. Titration experiments confirmed that the presence of Ca in Na 0.96 Ca 0.02 [Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 effectively suppressed the otherwise rigorous Na + /H + ion exchange in the presence of water. Charge/discharge cycling in half cells suggested that Ca improved the active material’s specific capacity and capacity retention by retaining its structural integrity and eliminating surface impurity formation during ambient electrode processing. Finally, the cycling performance of Na 0.96 Ca 0.02 [Ni 1/3 Fe 1/3 Mn 1/3 ]O 2 /hard carbon full cells was evaluated with upper cut-off voltages of 4.0 V and 4.1 V. Raising the upper cut-off voltage to 4.1 V resulted in a 20% gain in specific energy, but also accelerated capacity fade and voltage polarization, most likely due to an irreversible phase transition above 4.0 V.
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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 | 0.000 | 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".