Impact of Water on the Properties of Superconcentrated Electrolytes
Bibliographic record
Abstract
Superconcentrated electrolytes are a new class of electrolyte that is of interest to the scientific community for battery applications. These electrolytes consist of an amount of salt greater than the amount of solvent, while remaining liquid. It is currently accepted that the reduction of the solvation sphere under these conditions results in a greater electrochemical stability of the solvent and a higher Li+ transport number. These properties, are widely used in the literature For instance, superconcentrated electrolytes employing acetonitrile as the solvent have been applied in lithium metal batteries while acetonitrile with moderate salt concentration decomposes on lithium. Despite the attractiveness of the field to these electrolytes, their properties are still poorly understood, and the methods of analysis used vary between different studies. There is no standard protocol for their use and it becomes difficult to establish correlations between different studies. Our goal is to establish the required methods to standardize the methodology and fill the gaps in the field. To do so, we firstly studied superconcentrated electrolytes based on LiTFSI and acetonitrile. The approach taken highlights the factors that have a significant impact on the properties of electrolytes. One of these factors is the water content. The results demonstrate that the presence of water in the solutions has a limited impact on the physical properties of viscosity and density of the mixtures as long at the concentration remains at and below 1000 ppm. The water content has a significant impact on the electrochemical window of stability of the electrolyte, reducing it to less than 2 V at 1000 ppm. The solvating structure of superconcentrated electrolytes is also studied via the electrochemistry of the Ferrocene/Ferrocenium couple to probe the impact of the electrolyte properties on electron transfer reactions. These results highlight the importance of structure and composition in the development of superconcentrated electrolytes.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".