On the Relevance of Reporting Water Content in Highly Concentrated Electrolytes: The LiTFSI-Acetonitrile Case
Bibliographic record
Abstract
Highly concentrated electrolytes (HCE) are intensively studied as electrolytes in energy storage devices, with a focus on lithium-metal batteries. Despite the numerous combinations of solvent and salt reported, the relationships between the HCE composition and their properties are not fully understood, which hinders the use of more systematic approaches to their development. In order to address this need, we present here a study of the impact of water on the properties of HCE composed of LiTFSI salt and acetonitrile solvent. The physicochemical properties (density, viscosity and ionic conductivity) and on the electrochemical windows were determined for three electrolytes of different concentrations (1, 3 and 4.1 M) of LiTFSI in acetonitrile with different water contents (20, 200 and 1000 ppm). While the physicochemical properties are only depend on the salt concentration and not the water content, the latter has a significant effect on the electrochemistry of the electrolyte as the electrochemical windows decreased by up to 1.25 V for the 4.1 M HCE with 1000 ppm of water. These results highlight the fact than physicochemical properties cannot be used to assess the water levels and that even 200 ppm decreases the electrochemical windows of the electrolyte.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".