Determination of Mass Transfer Properties and Ionic Association in LiPF<sub>6</sub> - Organic Carbonates Solutions from PFG-NMR and Specific Conductivity Data
Bibliographic record
Abstract
We derive herein several mass transport properties (diffusion coefficients for cations, anions, and neutral ionic aggregates; cation transference number; salt association constant) of LiPF6 solutions in binary mixtures of ethylene carbonate (EC) and dimethyl carbonate (DMC) or ethyl methyl carbonate (EMC) through an analysis of PFG-NMR and specific conductivity data. Results were obtained as a function of salt concentration for EC:DMC 1:1 v/v, also from 5 ºC to 35 ºC for 1M LiPF6 dissolved in binary mixtures of EC with DMC and EMC at 3:7, 1:1 and 7:3 volume ratios. Li+ transference numbers have significantly lower values than the transport numbers, ranging from 0.31 to 0.35, and decrease with concentration. Under the assumptions that the higher order neutral aggregates beyond ion pairs can be neglected and that the intrinsic diffusion coefficients of cations and ion pairs are the same (valid only for solutions containing LiPF6, LiBF4 or LiClO4salt and significant amounts of EC), one can determine the degree of ion pairing as well as the intrinsic diffusion coefficients for cations, anions and ion pairs. A high degree of ion pairing (from 36% to 67%) was observed for the solutions investigated in the present work. Acknowledgements The authors acknowledge funding through the NSERC APC program and GM of Canada. Figure 1
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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 | 0.000 | 0.000 |
| 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".