Superionic Fluorinated Halide Solid Electrolytes for Highly Stable Li‐Metal in All‐Solid‐State Li Batteries
Bibliographic record
Abstract
Abstract The halide solid‐state electrolytes (SSEs) have received significant attention due to their high ionic conductivity and desirable compatibility with cathode materials. However, the reduction potential of the halide is still >0.6 V (versus Li/Li+). Reduction stability is still one of the challenges that need to be addressed. The fluorides have a wide electrochemical stability window due to the large electronegativity of F–. In contrast, Li3YBr6 (LYB) bromides have a narrower electrochemical window, although they have high lithium ion conductivity (>10–3 S cm–1). Herein, a fluorine doping strategy is employed. The interfacial stability between fluoride‐doped bromides and lithium metal is researched by cycling of lithium symmetric cells. Li plating/stripping can maintain over 1000 h at 0.75 mA cm–2. Interfacial protection mechanisms investigated by X‐ray photoelectron spectroscopy. A fluoride‐rich interfacial layer is formed in situ during the cycle, which achieves inhibition of the reduction. The Li metal treated fluorine doping of LYB exhibits significant potential in full cells. In fact, the induction of a stable in situ interfacial layer by fluorine doping can effectively improve the interfacial stability of bromides to lithium metal. Fluorine‐doped modification offers a new attempt to realize lithium metal applications in all‐solid‐state lithium batteries.
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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 | 0.001 |
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".