Solid Fluoride Electrolytes and Their Composite with Carbon: Issues and Challenges for Rechargeable Solid State Fluoride-Ion Batteries
Bibliographic record
Abstract
Solid-state batteries relying on fluoride-ion shuttle are still at their early stage of development. Assessing the fluoride solid electrolyte’s electrochemical stability and its conduction properties in a mixture with carbon, as well as the possible interaction of fluoride-ion with carbon both during the electrode preparation and upon electrochemical reactions, are mandatory to enable future practical applications. Here, we discuss these points using LaF 3 doped with BaF 2 (La 0.95 Ba 0.05 F 2.95, LBF) as a benchmark solid fluoride electrolyte. We establish that lithium may be used as a pseudoreference electrode to assess the electrochemical stability window of LBF and support the experiment with thermodynamic calculations. We demonstrate the chemical compatibility of LBF with carbon upon ball-milling and investigate the electrical conductivity of the formed LBF-C composite. We use a LBF|LBF-C|LBF cell (in this configuration, LBF serves as electronically blocking electrode) to assess the ionic conductivity of the LBF-C composite. The results confirm that both electronic and ionic percolations are ensured within the LBF-C composite despite a noticeable decrease of the ionic conductivity. Finally, we use a Li|LBF|LBF-C cell to evaluate the electrochemical fluorination of the carbon in the LBF-C composite. Our results suggest an electrochemical activity of carbon with fluoride ions. The possible interactions of carbon with fluorides to form insulating carbon fluoride (CF x ) must be considered when determining the operating voltage of FIBs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".