Strategy Formulation for Mitigating Capacity Fading of Na‐Layered Oxides
Bibliographic record
Abstract
Abstract The mechanisms underlying capacity fading during cycling in layered oxide cathode materials for sodium‐ion batteries remain inadequately understood. It is essential to elucidate the reasons and propose effective strategies. Here, the capacity‐fading mechanism of commercial NaFe 1/3 Mn 1/3 Ni 1/3 O 2 is due to the dissolution of iron ions. Additionally, the extraction of sodium ions (after the Fe 3+ /Fe 4+ reaction) lowers the energy level of NaFe₁/₃Mn₁/₃Ni₁/₃O₂ below that of the electrolyte solvent, thereby inducing solvent decomposition. We establish screening criteria for electrolyte additives through theoretical calculations to improve capacity retention. We identified a series of nitrogen‐containing Lewis base additives that can kinetically bind efficiently to iron ions in NaFe₁/₃Mn₁/₃Ni₁/₃O₂ and thermodynamically exhibit stronger electron‐donating abilities than the solvents. A new compound, sodium bis(trimethylsilyl)amide (which has not been studied as a Na‐ion battery additive before), is selected through the Reaxys database (out of 61 molecules) because it is commercially available at a low price and is relatively stable in the electrochemical process. Such an additive is demonstrated to greatly improve the Coulombic efficiency and reduce the dissolution of iron ions of NaFe₁/₃Mn₁/₃Ni₁/₃O₂//hard carbon cells.
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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".