Determination of Binary Diffusivities in Concentrated Lithium Battery Electrolytes via NMR and Conductivity Measurements
Bibliographic record
Abstract
Abstract We report on the experimental determination of Onsager–Stefan–Maxwell binary diffusivities using the pulsed-field gradient nuclear magnetic resonance (PFG NMR) technique, which are required for the concentrated solution theory to properly describe mass transport in practical liquid electrolytes for Li batteries. The ionic conductivity, calculated based on the obtained diffusivities, matches perfectly with the experimental values. We demonstrate the effectiveness of the approach using two test solutions of either lithium bis (trifluoromethanesulfonyl) imide or lithium bis (fluorosulfonyl) imide in tetraethylene glycol dimethyl ether in a temperature range of 20–50 °C. Accurate parametrization is achieved by reassessing the interpretation of PFG NMR-measured ionic diffusion coefficients. In particular, using the supporting theoretical background, we postulate that the diffusion coefficients represent an average of self-diffusion when the ions move independently and of the directionally correlated motion when the ions interact with each other even without the formation of neutral aggregates. A proper deconvolution of these two parts allows one to calculate the required binary diffusivities with high precision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".