Model-Based Prediction of Composition of an Unknown Blended Lithium-Ion Battery Cathode
Bibliographic record
Abstract
A model-based approach to accurately predict the composition of unknown blended Li-ion battery cathodes by fitting to experimental discharge curves is demonstrated. The electrochemically active constituents of the electrode are first determined by coupling information from low-rate galvanostatic lithiation data and SEM/EDX analyses of the electrode. The electrode composition is then estimated using a physics-based mathematical model of the electrode. The accuracy of this method has been assessed by comparison of the estimated composition with the value obtained from an independent, non-electrochemical experimental technique involving the deconvolution of XRD spectra. The electrode compositions obtained in these two ways are found to be in excellent agreement, within 1% of each other, demonstrating the promise of this new model-based approach. The method detailed in this work involves destructive and ex-situ testing, but only a relatively simple model is required to accurately determine the composition of a blended cathode in a Li-ion battery. This approach could also be useful for tracking the evolution of the blended electrode composition over the course of aging and gain a better understanding of the degradation mechanisms at play in cases where the active material loss contributes significantly to the overall capacity/power loss of the battery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".