Li-Rich and Halide-Deficient Argyrodite Fast Ion Conductors
Bibliographic record
Abstract
We report on a new family of halide-deficient and Li-rich argyrodite fast-ion conductors, Li 6+ x PS 5+ x (Cl/Br/I) 1– x (0 ≤ x ≤ 0.85). Exploration of the influence of aliovalent anion substitution in Li 6 PS 5 X (X = Cl, Br, I)─using a combination of high-resolution powder neutron diffraction and electrochemical impedance spectroscopy─reveals that aliovalent anion substitution induces higher Li-ion concentration and Li site disorder, and creates S 2– /I – anion site disorder on the 4 a site. In the series Li 6+ x PS 5+ x I 1– x (0 ≤ x ≤ 0.4), the resulting conductivity for Li 6.4 PS 5.4 I 0.6 (0.13 mS·cm –1 ) represents almost a 100-fold increase over that of the parent phase, Li 6 PS 5 I (0.0033 mS·cm –1 ), and establishes one of the first fast-ion conducting argyrodite thiophosphate iodides. For Cl-argyrodites, the ionic conductivity decreases a little with lower halide-content but ionic conductivity for the Br-argyrodites is almost unchanged. Overall, all Cl/Br-argyrodites Li 6+ x PS 5+ x (Cl/Br) 1– x (0 ≤ x ≤ 0.75) with a low halide content exhibit surprisingly high ionic conductivities > 1 mS·cm –1 despite a very low degree of sulfur/halogen anion site disorder. Our findings highlight the importance of attaining a disordered Li-ion sublattice and sulfur/halogen anion site disorder (anionic charge homogeneity) in argyrodites, where Li ions occupy high energy sites and activate concerted ion migration that drives the ionic conductivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".