Emerging Characterization Techniques for Electrode Interfaces in Sulfide‐Based All‐Solid‐State Lithium Batteries
Bibliographic record
Abstract
All‐solid‐state Li batteries (ASSLBs) are attracting increasing attentions due to their improved safety and high energy density compared with conventional liquid electrolyte‐based Li‐ion batteries (LIBs). ASSLBs based on sulfide solid‐state electrolytes (SEs) is one of the most popular categories, because sulfide SEs have a very competitive ionic conductivity (up to over 10−2 S cm−1 at room temperature), medium mechanical stiffness, decent contact with electrode materials, and negligible grain boundary resistance. However, interface problems between electrode materials and sulfide SEs seriously plague the development of high‐performance sulfide‐based ASSLBs. In‐depth understandings on the electrode interface problems are pivotal to propose and explore effective strategies to alleviate those issues. In recent years, diverse advanced characterization techniques have been developed, which deepen insights into the problematic interface from physical, chemical, electrochemical, and mechanochemical perspectives. Herein, electrode interfaces and their fundamental knowledge in sulfide‐based ASSLBs are first clarified. Second, various emerging characterizations are overviewed to illustrate the interfacial issues on both oxide cathode/sulfide SE and Li anode/sulfide SE interfaces. Meanwhile, advantages and disadvantages of each characterization techniques are explicated. Finally, an outlook of advanced characterizations that are specifically adapted for interface analysis in sulfide‐based ASSLBs is proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".