Halide Heterogeneous Structure Boosting Ionic Diffusion and High‐Voltage Stability of Sodium Superionic Conductors
Bibliographic record
Abstract
Abstract The development of solid‐state sodium‐ion batteries (SSSBs) heavily hinges on the development of an superionic Na+ conductor (SSC) that features high conductivity, (electro)chemical stability, and deformability. The construction of heterogeneous structures offers a promising approach to comprehensively enhancing these properties in a way that differs from traditional structural optimization. Here, this work exploits the structural variance between high‐ and low‐coordination halide frameworks to develop a new class of halide heterogeneous structure electrolytes (HSEs). The halide HSEs incorporating a UCl3‐type high‐coordination framework and amorphous low‐coordination phase achieves the highest Na+ conductivity (2.7 mS cm−1 at room temperature, RT) among halide SSCs so far. By discerning the individual contribution of the crystalline bulk, amorphous region, and interface, this work unravels the synergistic ion conduction within halide HSEs and provides a comprehensive explanation of the amorphization effect. More importantly, the excellent deformability, high‐voltage stability, and expandability of HSEs enable effective SSSB integration. Using a cold‐pressed cathode electrode composite of uncoated Na0.85Mn0.5Ni0.4Fe0.1O2 and HSEs, the SSSBs present stable cycle performance with a capacity retention of 91.0% after 100 cycles at 0.2 C.
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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".