Mapping of Lithium-Ion Battery Electrolyte Transport Properties and Limiting Currents with In Situ MRI
Bibliographic record
Abstract
Given the electrochemical modelling and control systems challenges facing lithium-ion batteries in extreme operating conditions, such as low temperature and high C-rate, it is important to understand the transport dynamics in a polarized cell with large electrolyte concentration gradients. To this end, a combination of conventional magnetic resonance imaging (MRI) experiments and MRI experiments coupled with pulsed-field gradient NMR for diffusion measurements were performed on an in situ lithium-ion cell operating at a variety of temperatures and current densities. The aim was to quantify the electrolyte transport parameters with spatial resolution. Some progress was attained towards this aim, and the necessary framework for future studies along this direction was developed; however, it was determined that in order to accurately quantify the transference number, a very accurate measurement of the concentration gradient is necessary when the polarization is large. It was also observed that limiting current behavior in the electrolyte at low temperature arises as a consequence of diffusion limitation on the anodic side, rather than ion depletion on the cathodic side. The framework developed herein may be useful not only for electrochemical model validation, but potentially also comprehensive electrolyte transport characterization, should the identified experimental limitations be overcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".