Synergistic Perovskite Titanate Coating and Lattice Doping Toward Air‐Stable and Long‐Life O3‐Type NaNi <sub>1/3</sub> Fe <sub>1/3</sub> Mn <sub>1/3</sub> O <sub>2</sub> Cathodes
Bibliographic record
Abstract
ABSTRACT O3‐type layered oxides are promising cathode materials for sodium‐ion batteries but suffer from structural instability, sluggish kinetics, and moisture sensitivity. This work proposes a synergistic modification of O3‐NaNi 1/3 Fe 1/3 Mn 1/3 O 2 (NFM). Among various perovskite titanates (CaTiO 3 , SrTiO 3 , BaTiO 3 ) investigated, CaTiO 3 proves to be the most effective modifier. DFT calculations reveal that Ca 2+ doping uniquely strengthens the Na‐O interaction, fundamentally enhancing the air stability. The conformal CaTiO 3 coating serves as a robust physical barrier against humid air, significantly suppressing the formation of surface carbonates and residual alkali. Simultaneously, Ca 2+ and Ti 4+ are doped into Na and transition metal (TM) sites, respectively, which enlarges the Na + layer spacing (from 3.701 to 3.812 Å), strengthens the TM─O framework, and mitigates irreversible phase transitions. As a result, the modified NFM@CTO06 cathode exhibits outstanding electrochemical performance, demonstrating a high capacity retention of 85.1% after 300 cycles at 1 C. Furthermore, it demonstrates reduced voltage hysteresis, enhanced Na + diffusion kinetics, and significantly improved air stability. This surface‐to‐bulk strategy offers a feasible approach toward practical high‐energy‐density sodium‐ion batteries.
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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".