Cubic Iodide Li<sub>x</sub>YI<sub>3+x</sub> Superionic Conductors through Defect Manipulation for All‐Solid‐State Li Batteries
Bibliographic record
Abstract
Abstract Halide solid electrolytes (SEs) have attracted significant attention due to their competitive ionic conductivity and good electrochemical stability. Among typical halide SEs (chlorides, bromides, and iodides), substantial efforts have been dedicated to chlorides or bromides, with iodide SEs receiving less attention. Nevertheless, compared with chlorides or bromides, iodides have both a softer Li sublattice and lower reduction limit, which enable iodides to possess potentially high ionic conductivity and intrinsic anti‐reduction stability, respectively. Herein, we report a new series of iodide SEs: LixYI3+x (x=2, 3, 4, or 9). Through synchrotron X‐ray/neutron diffraction characterizations and theoretical calculations, we revealed that the LixYI3+x SEs belong to the high‐symmetry cubic structure, and can accommodate abundant vacancies. By manipulating the defects in the iodide structure, balanced Li‐ion concentration and generated vacancies enables an optimized ionic conductivity of 1.04 × 10−3 S cm−1 at 25 °C for Li4YI7. Additionally, the promising Li‐metal compatibility of Li4YI7 is demonstrated via electrochemical characterizations (particularly all‐solid‐state Li‐S batteries) combined with interface molecular dynamics simulations. Our study on iodide SEs provides deep insights into the relation between high‐symmetry halide structures and ionic conduction, which can inspire future efforts to revitalize halide SEs.
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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".