Reduction of a Lithium-Ion Solid Electrolyte Model Under Potentiostatic Hold
Bibliographic record
Abstract
Solid state electrolytes are becomingly increasingly attractive due to their improved safety, lower self-discharge, and higher power densities over the more commonly used liquid electrolyte [1,2]. Solid electrolytes are a relatively new material in the field of lithium-ion batteries, therefore there is considerably less mathematical investigation and analysis into these materials, compared with their liquid counterparts. We review a model for a solid electrolyte from thermodynamics principles [3]. We non-dimensionalise and scale the model to identify small parameters, where we identify a scaling that widens the boundary layer in the electrolyte compared to previous literature. We consider an Li2O solid electrolyte which is attached on each end by blocks of solid lithium. We fix a potential difference across the electrolyte and consider different charge flux conditions. We present asymptotic analysis and numerical solutions for both the zero-charge flux equilibrium and for the non-zero charge flux equilibrium problems. We consider numerical simulations of the full-time dependent problem and show that there are two important time scales in the problem, an early transient timescale where the boundary layers first approach their equilibrium followed by a longer timescale where we observe the transience in the bulk of the solution. We briefly talk about the application of a solid electrolyte to cylindrical nanowire electrodes. Recent advances in electrode chemistry have allowed for cylindrical nanowire geometries exhibiting low-capacity fade. However, the performance of these electrodes is very sensitive to experimental conditions and early charge behavior and thus it is important to understand the lithium transport process in these systems. Following from this work, we will combine the solid electrolyte model with a nanowire electrode in order to investigate these processes. References [1] A. Manthiram, X. Yu, Xingwen, and S. Wang. Lithium battery chemistries enabled by solid-state electrolytes, Nature Reviews Materials, Nature Publishing Group, 2(4), 1-16, 2017. [2] Z. Chen, G. Kim, Z. Wang, D. Bresser, B. Qin, D. Geiger, U. Kaiser, X. Wang, Z.X. Shen, and S. Passerini. 4-V flexible all-solid-state lithium polymer batteries, Nano Energy, Elsevier, 64, 103986, 2019. [3] S. Braun, C. Yada, and A. Latz. Thermodynamically consistent model for space-charge-layer formation in a solid electrolyte, The Journal of Physical Chemistry C, 119(39), 22281-22288, 2015.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".