Crystal Structures and Electrochemical Performance of Air-Stable Na<sub>2/3</sub>Ni<sub>1/3–<i>x</i></sub>Cu<sub><i>x</i></sub>Mn<sub>2/3</sub>O<sub>2</sub> in Sodium Cells
Bibliographic record
Abstract
Sodium ion batteries have garnered significant research attention in recent years due to the rising demand for large-scale energy storage solutions as well as the high abundance of sodium. P2-type layered oxide materials have been identified as promising positive electrode materials for sodium ion batteries. Previously, P2–Na 2/3 Ni 1/3 Mn 2/3 O 2 was shown to have a high operating voltage and high capacity but suffers from a step-like voltage curve and capacity loss during cycling, potentially due to its P2–O2 transition at high voltages. One strategy to improve cycling performance has been to dope Ni 2+ with other 2+ cations, such as Zn 2+ or Mg 2+, which improved capacity retention but significantly decreases reversible capacity, since these ions were not electrochemically active. Since Cu 2+ has been shown to be electrochemically active, we replaced Ni 2+ with Cu 2+, resulting in air-stable Na 2/3 Ni 1/3– x Cu x Mn 2/3 O 2 (0 ≤ x ≤ 1/3). Both Ni 2+ /Ni 4+ and Cu 2+ /Cu 3+ participate in the redox reaction during cycling, capacity retention was greatly improved, and phase changes were suppressed during cycling without sacrificing much capacity. The material retains a P2/OP4 structure even when cycled to high voltages. The doping strategy is a promising approach for the future development of positive electrode materials for sodium ion batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".