Multiple scales homogenisation of a porous viscoelastic material with rigid inclusions: Application to lithium-ion battery electrodes
Bibliographic record
Abstract
This paper explores the mechanical behaviour of the composite materials used in modern lithium-ion battery electrodes. These contain relatively high modulus active particle inclusions within a two-component matrix of liquid electrolyte which penetrates the pore space within a viscoelastic polymer binder . Deformations are driven by a combination of (i) swelling/contraction of the electrode particles in response to lithium insertion/extraction, (ii) swelling of the binder as it absorbs electrolyte, (iii) external loading and (iv) flow of the electrolyte within the pores. We derive the macroscale response of the composite using systematic multiple scales homogenisation by exploiting the disparity in lengthscales associated with the size of an electrode particle and the electrode as a whole. The resulting effective model accurately replicates the behaviour of the original model (as is demonstrated by a series of relevant case studies) but, crucially, is markedly simpler and hence cheaper to solve. This has significant practical value because it facilitates low-cost, realistic computations of the mechanical states of battery electrodes, thereby allowing model-assisted development of battery designs that are better able to withstand the mechanical abuse encountered in practice and ultimately paving the way for longer-lasting batteries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".