Continuous medium approach to approximate the high concentrated aqueous electrolyte with different types of electrochemical structure
Bibliographic record
Abstract
Superconcentrated aqueous electrolytes have recently emerged as a new class of electrolytes, called water-in-salt electrolytes. They are distinguished, in both weight and volume, by a quantity of salt greater than water. Currently, these electrolytes are attracting major interest, particularly for application in aqueous rechargeable batteries. These electrolytes have only a small amount of free water due to an ultrahigh salt concentration. Consequently, the electrochemical stability window of water is wider than the predicted thermodynamic value of 1.23 V. Hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) have been shown to be shifted to more negative and positive potentials, respectively. The decrease in free water population is recognized as being involved in the increase in the electrochemical stability window of water. Here, we study the quantitative contribution of the decrease in the free water molecule concentration to the permittivity of the solution and of the activity of water to the OER and HER overpotentials when the salt concentration increases. We compare our model with that of Kornyshev and get three types of electrolyte structures: diluted, gradient of water contents, and aggregation. The theoretical calculation of the redox potentials of the OER and HER is compared with the experimentally determined electrochemical properties of aqueous LiTFSI 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".