Entropy Tuning Stabilizing P2‐Type Layered Cathodes for Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract The P2‐type layered transition metal oxide cathodes confront formidable challenges, including irreversible deleterious phase transitions, transition metals migration, and sluggish Na + diffusion kinetics, which hamper their rapid commercial application in sodium ion batteries (SIB). In this work, an entropy tuning with dual‐site substitution strategy is proposed to address the aforementioned issues. In the tailored [Na 0.67 Zn 0.05 ]Ni 0.22 Cu 0.06 Mn 0.66 Ti 0.01 O 2 (NZNCMTO) cathodes, the strategic incorporation of Zn ions serves to occupy Na sites, intentionally disrupting the Na/vacancy ordering and establishing a reinforcing “pillar” effect within the layered framework. Furthermore, the substitution of Cu and Ti for Ni and Mn bolsters covalent bonding with the lattice oxygen, thereby impeding the migration of the transition metal ions and leading to a near‐zero strain structural evolution during charge and discharge process. Density functional theory calculations confirmed that entropy‐tuned NZNCMTO substantially lowered the migration energy barrier for Na + ions diffusion and improved electronic conductivity. Consequently, the NZNCMTO cathode exhibits an impressive high practical capacity of 91.54 mAh g −1 at a high discharge rate of 10 C, along with outstanding cycling stability, maintaining near 100% capacity retention over 500 cycles at a current density of 10 C. This work presents an innovative blueprint for designing high‐performance sodium‐ion battery cathode materials.
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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".