Enhanced Performance of P2‐Na<sub>0.66</sub>(Mn<sub>0.54</sub>Co<sub>0.13</sub>Ni<sub>0.13</sub>)O<sub>2</sub> Cathode for Sodium‐Ion Batteries by Ultrathin Metal Oxide Coatings via Atomic Layer Deposition
Bibliographic record
Abstract
Sodium‐ion batteries are widely considered as promising energy storage systems for large‐scale applications, but their relatively low energy density hinders further practical applications. Developing high‐voltage cathode materials is an effective approach to increase the overall energy density of sodium‐ion batteries. When cut‐off voltage is elevated over 4.3 V, however, the cathode becomes extremely unstable due to structural transformations as well as metal dissolution into the electrolytes. In this work, the cyclic stability of P2‐Na 0.66 (Mn 0.54 Co 0.13 Ni 0.13 )O 2 (MCN) electrode at a cut‐off voltage of 4.5 V is successfully improved by using ultrathin metal oxide surface coatings (Al 2 O 3 , ZrO 2 , and TiO 2 ) deposited by an atomic layer deposition technique. The MCN electrode coated with the Al 2 O 3 layer exhibits higher capacity retention among the MCN electrodes. Moreover, the rate performance of the MCN electrode is greatly improved by the metal oxide coatings in the order of TiO 2 < Al 2 O 3 < ZrO 2 , due to increased fracture toughness and electrical conductivity of the metal oxide coating layers. A ZrO 2 ‐coated MCN electrode shows a discharge capacity of 83 mAh g −1 at 2.4 A g −1 , in comparison to 61 mAh g −1 for a pristine MCN electrode. Cyclic voltammetry and electrochemical impedance analysis disclose the reduced charge transfer resistance from 1421 to 760.2 Ω after cycles, suggesting that the metal oxide coating layer can effectively minimize the undesirable phase transition, buffer inherent stress and strain between the binder, cathode, and current collector, and avoid volumetric changes, thus increasing the cyclic stability of the MCN electrode.
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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) | 0.001 | 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 | 0.001 | 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".