Pressure Tuning and Sn Particle Size Optimization for Enhanced Performance in PbSnF<sub>4</sub>‐Based All‐Solid‐State Fluoride Ion Batteries
Bibliographic record
Abstract
Abstract All‐solid‐state fluoride ion batteries (ASSFIBs) show remarkable potential as energy storage devices due to their low cost, superior safety, and high energy density. However, the poor ionic conductivity of F − conductor, large volume expansion, and the lack of a suitable anode inhibit their development. In this work, PbSnF 4 solid electrolytes in different phases ( β ‐ and γ ‐PbSnF 4 ) are successfully synthesized and characterized. The ASSFIBs composed of β ‐PbSnF 4 electrolytes, a BiF 3 cathode, and micrometer/nanometer size ( µ ‐/ n ‐) Sn anodes, exhibit substantial capacities. Compared to the μ ‐Sn anode, the n ‐Sn anode with nanostructure exhibits superior battery performance in the BiF 3 / β ‐PbSnF 4 /Sn battery. The optimized battery delivers a high initial discharge capacity of 181.3 mAh g −1 at 8 mA g −1 and can be reversibly cycled at 40 mA g −1 with a high discharge capacity of over 100.0 mAh g −1 after 120 cycles at room temperature. Additionally, it displays high discharge capacities over 90.0 mAh g −1 with excellent cyclability over 100 cycles under ‐20 °C. Detailed characterization has confirmed that reducing Sn particle size and boosting external pressure are crucial for achieving good defluorination/fluorination behaviors in the Sn anode. These findings pave the way to designing ASSFIBs with high capacities and superior cyclability under different operating temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".