Deciphering the Role of Fluorination in Dual‐Halogen Electrolytes for All‐Solid‐State Batteries: A Case Study of New Li<sub>2</sub>HfCl<sub>6−x</sub>F<sub>x</sub> Solid Electrolytes
Bibliographic record
Abstract
Abstract Lithium metal chlorides are promising superionic conductors for all‐solid‐state batteries (SSBs) due to their favorable mechanical properties, high ionic conductivity, and good oxidative stability (up to >4.2 V versus Li/Li + ). Nonetheless, chloride solid electrolytes (SEs) still undergo electrochemical degradation when paired with high‐voltage cathodes such as LiNi 0.85 Co 0.1 Mn 0.05 O 2 . A viable strategy to enhance the intrinsic electrochemical stability of chloride electrolytes is to partially substitute Cl with F. By leveraging complementary insights from neutron and X‐ray diffraction, X‐ray absorption spectroscopy, X‐ray photoelectron spectroscopy (XPS), time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), and electrochemical studies, we investigate the interplay between ionic and electronic conductivity, voltage stability, and overall battery performance of a family of new dual‐halogen SEs—Li 2 HfCl 6−x F x . All‐solid‐state cells utilizing Li 2 HfCl 5.5 F 0.5 as the electrolyte demonstrate much‐enhanced battery performance compared to Li 2 HfCl 6 . This improvement is mainly attributed to the formation of a kinetically stable LiF‐rich cathode electrolyte interphase (CEI), which inhibits detrimental reactions between the cathode and the SE, as revealed by ToF‐SIMS studies. The findings from this study are applicable to other dual‐halogen solid ionic conductors, offering valuable insights into the relationship between intrinsic electrochemical window (IEW), electronic and ionic conductivity, and battery performance in dual‐halogen solid‐state electrolytes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".