On the Correlation between Free Volume, Phase Diagram and Ionic Conductivity of Aqueous and Non-Aqueous Lithium Battery Electrolyte Solutions over a Wide Concentration Range
Bibliographic record
Abstract
There is little known about the transport behavior of ions in electrolyte solutions at very high concentrations and there is currently no one widely-accepted theory or equation to describe it over the whole concentration range. In this work, the ionic conductivity (κ) of lithium salts in aqueous and non-aqueous electrolyte solutions have been measured as a function of concentration (C) and have been fitted to known theoretical and empirical equations. A new, isothermal, semi-empirical equation based on free volume theory: where V o and V f are the occupied and unoccupied "free" volume, respectively, gives better fit over the whole concentration range than the known equations. V f and V 0 2, ϕ , the apparent molar volume of the salt, were calculated from density measurements and it is found that free volume decreases with concentration in both the aqueous and non-aqueous solutions over the whole range. We hypothesize that the changes to transport properties in solution with concentration are caused by structural changes that switches the conductivity mechanism from vehicular to a Grotthuss-type or a mixture of both. We, for the first time, correlate the origin of C max , the concentration of highest conductivity, to the eutectic composition in the salt-solvent phase diagram.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".