Pore Network Modelling of Galvanostatic Discharge Behaviour of Lithium-Ion Battery Cathodes
Bibliographic record
Abstract
The performance of Lithium-Ion batteries (LIB’s) strongly depends on 3D microstructure and continued research is needed for the development and optimization of electrode designs to further reduce cost and improve performance and durability. In this work, a pore network modelling approach is presented to understand the structure-performance relationship of porous cathodes of LIB’s. It was demonstrated that pore network models can efficiently predict the rate-dependent capacity of an electrode using only a 3-phase tomogram as input. The developed modelling framework was used to perform structural analysis on two Li(Ni0.5Mn0.3Co0.2)O2 (NMC532) cathodes of different thickness and calendaring pressure and revealed important insights of microstructural heterogeneities inside porous structures, including spatial distribution of concentration, potential and state of lithiation in electrolyte, active material and carbon binder domain. The computational performance of the pore network model was analyzed, and excellent performance was demonstrated, taking hours instead of weeks for a similar direct numerical simulation. The novel modelling framework reported in this study will enable the study of local heterogeneities in other types of cathode material to help screen next-generation electrode designs, augmenting and informing time-consuming cell fabrication and laboratory testing.
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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 | 0.000 |
| Open science | 0.001 | 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".