Site-Occupation-Tuned Superionic Li<sub><i>x</i></sub>ScCl<sub>3+<i>x</i></sub>Halide Solid Electrolytes for All-Solid-State Batteries
Bibliographic record
Abstract
The enabling of high energy density of all-solid-state lithium batteries (ASSLBs) requires the development of highly Li + -conductive solid-state electrolytes (SSEs) with good chemical and electrochemical stability. Recently, halide SSEs based on different material design principles have opened new opportunities for ASSLBs. Here, we discovered a series of Li x ScCl 3+ x SSEs ( x = 2.5, 3, 3.5, and 4) based on the cubic close-packed anion sublattice with room-temperature ionic conductivities up to 3 × 10 –3 S cm –1 . Owing to the low eutectic temperature between LiCl and ScCl 3, Li x ScCl 3+ x SSEs can be synthesized by a simple co-melting strategy. Preferred orientation is observed for all the samples. The influence of the value of x in Li x ScCl 3+ x on the structure and Li + diffusivity were systematically explored. With increasing x value, higher Li +, lower vacancy concentration, and less blocking effects from Sc ions are achieved, enabling the ability to tune the Li + migration. The electrochemical performance shows that Li 3 ScCl 6 possesses a wide electrochemical window of 0.9–4.3 V vs Li + /Li, stable electrochemical plating/stripping of Li for over 2500 h, as well as good compatibility with LiCoO 2 . LiCoO 2 /Li 3 ScCl 6 /In ASSLB exhibits a reversible capacity of 104.5 mAh g –1 with good cycle life retention for 160 cycles. The observed changes in the ionic conductivity and tuning of the site occupations provide an additional approach toward the design of better SSEs.
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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".