Effective Transport Properties of Porous Electrochemical Materials — A Homogenization Approach
Bibliographic record
Abstract
This study concerns the determination of effective transport coefficients for multiscale composite materials used in various electrochemical systems. The effective transport coefficients are indispensable in macroscale modeling of such systems. We propose an integrated approach which for a given two-phase or three-phase microstructure allows us to systematically determine the exact values of different effective transport coefficients such as diffusivity of a species or electric conductivity. In addition to electron microscopy, this approach combines state-of-the-art techniques of mathematical homogenization, image processing and numerical computation. When only partial information about the microstructure is available, rigorous upper and lower bounds are available on the effective transport coefficients and we demonstrate that the commonly used Bruggeman's formula may in fact violate the lower bound in some regimes. These upper bounds also allow one to quantify how much the transport properties of the material with a given composition could be improved. The proposed approach is illustrated by analyzing a three-phase electrode material of an actual Li-ion battery. We also quantify the uncertainty of the effective transport coefficients resulting from possibly imprecise information about the material properties of the individual phases and address the question concerning the importance of resolving all three phases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".