The Role of Li Salt in Highly Concentrated Electrolyte of Lithium Sulfur Batteries
Bibliographic record
Abstract
Recently, the properties of electrolyte have been reported as powerful parameter affecting the behavior of Li ion battery. Among them, Li salt has been regarded as important parameter that can influence not only the battery performance but also Li metal stabilization. In the same line with this view, highly concentrated salt has been attracted because of its various unusual functionalities such as high electrochemical stability for advanced lithium ion battery. However, even though highly concentrated electrolyte has many advantages for advanced Li ion batteries, it cannot be applied in Li-S batteries due to the limited solubility of sulfur related species. In this paper, we applied different two types of salt in highly concentrated electrolyte for Li-S batteries. The combination of salt with electro-donating property changed the Li2S deposition chemistry and increased equilibrium concentration of polysulfides in electrolyte as well as Li metal stabilization. This unique effect of salt was clearly demonstrated using ex-situ analysis including Uv-vis, Raman spectroscopy with combination of computational simulation. This finding offers understanding about peculiar solvation structure under highly concentrated electrolyte and novel strategy for modulating electrochemical reduction mechanism in highly concentrated Li-S batteries
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".