A Critical Review for an Accurate Electrochemical Stability Window Measurement of Solid Polymer and Composite Electrolytes
Bibliographic record
Abstract
All-solid-state lithium batteries (ASSLB) are very promising for the future development of next generation lithium battery systems due to their increased energy density and improved safety. ASSLB employing Solid Polymer Electrolytes (SPE) and Solid Composite Electrolytes (SCE) in particular have attracted significant attention. Among the several expected requirements for a battery system (high ionic conductivity, safety, mechanical stability), increasing the energy density and the cycle life relies on the electrochemical stability window of the SPE or SCE. Most published works target the importance of ionic conductivity (undoubtedly a crucial parameter) and often identify the Electrochemical Stability Window (ESW) of the electrolyte as a secondary parameter. In this review, we first present a summary of recent publications on SPE and SCE with a particular focus on the analysis of their electrochemical stability. The goal of the second part is to propose a review of optimized and improved electrochemical methods, leading to a better understanding and a better evaluation of the ESW of the SPE and the SCE which is, once again, a critical parameter for high stability and high performance ASSLB applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".