Parametric analysis using impedance spectroscopy: relationship between material properties and battery performance
Bibliographic record
Abstract
The feasibility of predicting battery material performance based on parametrization of impedance spectra measured at different states of charge in boundaries of non-linear equivalent circuit model is demonstrated. All kinetically relevant parameters of LiCoO{sub 2}-based composite lithium ion battery cathode materials were obtained by an impedance parametrization procedure developed in the Kumho Chemical Laboratories in Korea. Accuracy of performance prediction was tested by comparing voltage profiles, calculated on the basis of numerical image, at discharge rates ranging from 1/10a to 3C with experimental data. The relative influence of kinetic steps on discharge behaviour of composite electrode were compared to model parameters. Thickness dependence predicted by the model was found to be correct when compared to experimental impedance spectra of samples with different thicknesses. It was concluded that multi-dimensional impedance parametrization showed good potential for use in battery material standardization and performance evaluation, as well as in optimizing material composites for specific applications. 14 refs., 10 figs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".