Niobium-doped layered cathode material for high-power and low-temperature sodium-ion batteries
Bibliographic record
Abstract
Abstract The application of sodium-based batteries in grid-scale energy storage requires electrode materials that facilitate fast and stable charge storage at various temperatures. However, this goal is not entirely achievable in the case of P2-type layered transition-metal oxides because of the sluggish kinetics and unfavorable electrode|electrolyte interphase formation. To circumvent these issues, we propose a P2-type Na 0.78 Ni 0.31 Mn 0.67 Nb 0.02 O 2 (P2-NaMNNb) cathode active material where the niobium doping enables reduction in the electronic band gap and ionic diffusion energy barrier while favoring the Na-ion mobility. Via physicochemical characterizations and theoretical calculations, we demonstrate that the niobium induces atomic scale surface reorganization, hindering metal dissolution from the cathode into the electrolyte. We also report the testing of the cathode material in coin cell configuration using Na metal or hard carbon as anode active materials and ether-based electrolyte solutions. Interestingly, the Na||P2-NaMNNb cell can be cycled up to 9.2 A g −1 (50 C), showing a discharge capacity of approximately 65 mAh g −1 at 25 °C. Furthermore, the Na||P2-NaMNNb cell can also be charged/discharged for 1800 cycles at 368 mA g −1 and −40 °C, demonstrating a capacity retention of approximately 76% and a final discharge capacity of approximately 70 mAh g −1 .
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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 | 0.001 |
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".