A Study of the Transport Properties of Ethylene Carbonate-Free Li Electrolytes
Bibliographic record
Abstract
Recently it has been found that Li-ion cells with organic carbonate-based electrolytes that do not contain ethylene carbonate (EC) can perform exceptionally well at high voltage. This work explores the transport properties of low-EC and EC-free Li electrolytes with lithium hexafluorophosphate (LiPF 6 ) as the conducting salt. Conductivity and viscosity were measured for electrolytes with solvent compositions EC:linear carbonate in a weight ratio of x:(100-x), where linear carbonate = {ethyl methyl carbonate (EMC), dimethyl carbonate (DMC)}, and x = {0, 10, 20, 30}. While EC-free electrolytes have lower viscosities, the maximum conductivities of these electrolytes are lower as well. Walden analysis was employed to understand the loss in conductivity as EC is removed from the electrolyte. Electrolyte properties calculated from a theoretical model, the Advanced Electrolyte Model (AEM) show excellent agreement with most of the experimental data. Differential thermal analysis (DTA) was used to investigate the phase diagram of the ternary EC:DMC:LiPF 6 system. The addition of both EC and LiPF 6 in the ranges studied lowered the liquidus transition temperature of the solution. Li[Ni 0.4 Mn 0.4 Co 0.2 ]O 2 /graphite and single crystal Li[Ni 0.5 Mn 0.3 Co 0.2 ]O 2 /graphite cells containing an EMC-based electrolyte showed good capacity retention and comparably low impedance growth when 5% w/w of fluoroethylene carbonate (FEC) was added.
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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 | 0.001 |
| 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".