Visualization of Steady-State Ionic Concentration Profiles Formed in Electrolytes during Li-Ion Battery Operation and Determination of Mass-Transport Properties by <i>in Situ</i> Magnetic Resonance Imaging
Bibliographic record
Abstract
Accurate modeling of Li-ion batteries performance, particularly during the transient conditions experienced in automotive applications, requires knowledge of electrolyte transport properties (ionic conductivity κ, salt diffusivity D, and lithium ion transference number t(+)) over a wide range of salt concentrations and temperatures. While specific conductivity data can be easily obtained with modern computerized instrumentation, this is not the case for D and t(+). A combination of NMR and MRI techniques was used to solve the problem. The main advantage of such an approach over classical electrochemical methods is its ability to provide spatially resolved details regarding the chemical and dynamic features of charged species in solution, hence the ability to present a more accurate characterization of processes in an electrolyte under operational conditions. We demonstrate herein data on ion transport properties (D and t(+)) of concentrated LiPF6 solutions in a binary ethylene carbonate (EC)-dimethyl carbonate (DMC) 1:1 v/v solvent mixture, obtained by the proposed technique. The buildup of steady-state (time-invariant) ion concentration profiles during galvanostatic experiments with graphite-lithium metal cells containing the electrolyte was monitored by pure phase-encoding single point imaging MRI. We then derived the salt diffusivity and Li(+) transference number over the salt concentration range 0.78-1.27 M from a pseudo-3D combined PFG-NMR and MRI technique. The results obtained with our novel methodology agree with those obtained by electrochemical methods, but in contrast to them, the concentration dependences of salt diffusivity and Li(+) transference number were obtained simultaneously within the single in situ experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".