Study of Effective Electrical Conductivity of Additive Free Electrodes Using a Homogenization Method
Bibliographic record
Abstract
Conductive additives are used in the cathode of a Li-ion battery to improve electrical conductivity.However, these additives can negatively impact the ionic conductivity and specific capacity of the battery.Therefore, design of additive-free cathodes is gaining attention in the research community.In this paper, we explore the effective electrical conductivity of randomly generated two-phase conductive-free cathode microstructures using a mathematical homogenization method.Over thousand microstructures with various combinations of particle size, volume fraction and conductivity ratios are considered to evaluate effective electrical conductivity values using this method.An explicit formulation is proposed based on the results to provide a simple method for evaluation of the effective conductivity values.The intrinsic properties of each phase of the microstructure are used to obtain the effective electrical conductivity values.With the microstructure geometry information being utilized for the evaluation of the effective properties, the results obtained from this formulation are expected to be more accurate and reliable than those obtained using the popular Bruggeman's approximation, providing better estimates of discharge characteristics.Finally, the significance of incorporation of micro-structural information to model cathodes is highlighted by studying the discharge characteristics of Li-ion battery system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".