Multi-Temperature<i>in Situ</i>Magnetic Resonance Imaging of Polarization and Salt Precipitation in Lithium-Ion Battery Electrolytes
Bibliographic record
Abstract
Accurate electrochemical modeling of lithium-ion batteries is an important direction in the development of battery management systems in automotive applications, for both real-time performance control and long-term state-of-health monitoring. Measurements of electrolyte-domain transport parameters, under a wide regime of temperature and current conditions, are a crucial aspect of the parametrization and validation of these models. This study constitutes the first exploration of the temperature dependence for the steady-state electrolyte concentration gradient under applied current with spatial resolution via the in situ magnetic resonance imaging (MRI) technique. The use of complementary pure phase-encoding MRI methods was found to provide quantitatively accurate measurements of the concentration gradient, in strong agreement with predictions based on ex situ NMR and electrochemical techniques. Temperature is demonstrated to have a marked influence on the steady-state concentration gradient as well as the rate of its buildup. This finding underlines the importance of utilizing spatially varying electrolyte transport parameters in modeling approaches. Additionally, a surprising outcome of this investigation was that a conventional 1.00 M LiPF 6 electrolyte in an equal-parts ethylene carbonate/diethylene carbonate solvent mixture generated salt precipitation under polarization at 10 °C. The loss of salt under strong polarization and at low temperature is a previously unaddressed potential source of long-term capacity fade in lithium-ion batteries, and on the basis of this result, warrants further investigation.
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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 | 0.000 |
| 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) | 0.001 | 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".