Design of Sodium Chalcohalide Solid Electrolytes with Mixed Anions for All‐Solid‐State Sodium‐Ion Batteries
Bibliographic record
Abstract
Abstract Solid‐state sodium‐ion batteries (SSNIBs) have emerged as a promising alternative to lithium‐ion systems for grid‐scale energy storage, owing to sodium's abundance and the improved safety of solid‐state designs. Among various solid‐state electrolytes (SSEs), halide‐based Na + SSEs offer high electrochemical stability but are limited by low ionic conductivity and poor thermal stability. Herein, a novel class of sodium hafnium chalcohalide SSEs is reported with a dual‐anion (S 2− /Cl − ) framework, with a high ionic conductivity of 4.5 × 10 −4 S cm −1 . The incorporation of sulfur enhances Na⁺ mobility by reducing the migration barrier through increased anion polarizability and expanded diffusion pathways. Additionally, S 2 − contributes to stronger interatomic bonding, leading to higher cohesive energy density, improved thermal stability, and mechanical robustness. These SSEs exhibit minimal sulfur oxidation and excellent chemical/electrochemical interface stability with different cathode materials, such as O3‐layered NaNi 1/3 Fe 1/3 Mn 1/3 O 2 , P2/O3 layered Na 0.85 Mn 0.5 Ni 0.4 Fe 0.1 O 2 , and Na 3 V 2 (PO 4 ) 3 cathodes. As a result, SSNIBs with P2/O3 layered Na 0.85 Mn 0.5 Ni 0.4 Fe 0.1 O 2 employing the sodium hafnium chalcohalide SSEs demonstrate outstanding cycling performance, achieving a capacity retention of 88.5% after 200 cycles at 0.1 C. This study establishes a new design strategy for high‐performance SSEs, demonstrating that mixed‐anion frameworks offer a viable route to overcome the intrinsic limitations of single‐anion electrolytes in next‐generation SSNIBs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".