Systematic Exploration of the Benefits of Ni Substitution in Na–Fe–Mn–O Cathodes
Bibliographic record
Abstract
Abstract Na‐ion batteries (SIBs) are receiving a great deal of attention as potential sustainable replacements for Li‐ion batteries in electric vehicles and grid storage applications. To date, commercialized SIBs offer inferior energy density with passable extended cycling. By contrast, next‐generation SIBs will likely utilize layered oxide cathodes that offer improved energy density but to date show inferior stability both during cycling and in terms of stability of the cathodes in air during cell assembly. These properties are highly tunable with composition and herein the promising P2 phases are systematically explored in the Na–Fe–Mn–O phase diagram by making 256 different compositions. The optimal material is a P2 material saturated with Ni (a modest 16% of the transition metal layer) and shows a highly competitive energy density of 640 Wh kg−1 while minimizing the amount of sacrificial sodium needed in full cells and also improving the air stability of the material. This study shows the vital role that thorough systematic screening will play in the continued development of these vital materials for sustainable secondary battery production and provides guidance toward sustainable Na‐ion cathodes by minimizing the nickel content required for high performance.
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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".