Designs of Experiments to Optimize Li-Ion Batteries
Bibliographic record
Abstract
Studies pinpointed the limitation of power-related performance for Li-ion batteries to the microstructure of each electrode.1, 2 This random arrangement of the active material (AM) and carbon filling particles bound by a polymer is typically characterized through physical values such as tortuosity, porosity and Mac Mullin Number.3 There is nonetheless no consensus on what the best formulation is for a given set of electrode components. The relationship between microstructure and performance is investigated by planning and analyzing a Design of Experiments based on a Complex Mixture Design. Thirty different formulations were characterized where Li4Ti5O12 was the AM, and carbon black and carbon nanofibers were conductive fillers. As for the binder, two were studied: polyvinylidene fluoride and a fluorine-free thermoplastic elastomer.4 All other factors, e.g. rheology or experimenter bias, were closely monitored and finely controlled to remain identical for all samples. Electrochemical performance were studied at low, medium and high charging speeds to account for different limitations of full capacity retention. Statistical analysis showed clear correlations between the formulation and the electrodes’ capacity with very high descriptive statistics, e.g. R2. Lastly, strong correlations were found between capacity and microstructure, strengthening further the trust in the empirical equations. These robust models helped choosing optimal fluorine-free formulations that surpassed even the highest performing previous electrodes. 1. Vasileiadis, A.; Klerk, N. J. J. d.; Smith, R. B.; Ganapathy, S.; Harks, P. P. R. M. L.; Bazant, M. Z.; Wagemaker, M., Toward Optimal Performance and In‐Depth Understanding of Spinel Li4Ti5O12 Electrodes through Phase Field Modeling. Advanced Functional Materials 2018, 0 (0), 1705992. 2. Ngandjong, A. C.; Rucci, A.; Maiza, M.; Shukla, G.; Vazquez-Arenas, J.; Franco, A. A., Multiscale Simulation Platform Linking Lithium Ion Battery Electrode Fabrication Process with Performance at the Cell Level. The Journal of Physical Chemistry Letters 2017, 8 (23), 5966-5972. 3. Landesfeind, J.; Hattendorff, J.; Ehrl, A.; Wall, W. A.; Gasteiger, H. A., Tortuosity Determination of Battery Electrodes and Separators by Impedance Spectroscopy. Journal of The Electrochemical Society 2016, 163 (7), A1373-A1387. 4. Rynne, O.; Lepage, D.; Aymé-Perrot, D.; Rochefort, D.; Dollé, M., Application of a Commercially-Available Fluorine-Free Thermoplastic Elastomer as a Binder for High-Power Li-Ion Battery Electrodes. Journal of The Electrochemical Society 2019, 166 (6), A1140-A1146. With this presentation, we want to show the versatility and power of Designs of Experiments to the community, whether for electrode formulation or new material synthesis, as the input parameters can be easily interchangeable. Figure 1
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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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".