Influence of the Formulation on the Microstructure and Thus Performance of Li-Ion Batteries
Bibliographic record
Abstract
The research in the field of Li-ion batteries is stronger than ever, with a common aim of increasing the energy and power density. More and more studies pinpoint the limitation of power-related performance for Li-ion batteries to the microstructure of each electrode. This random arrangement of the active material and carbon filling particles bound by the binder is typically characterized through physical values such as tortuosity, porosity and MacMullin Number. To investigate the relationship between microstructure and performance in details, we used a design of experiment to evaluate close to seventy different formulations with LiFePO4 and Li4Ti5O12 as the active materials (AM), carbon black (CB) and carbon nanofibers (CNF) as the conductive fillers, and two different binders: polyvinylidene fluoride and a thermoplastic elastomer. By keeping the loading and porosity constant throughout the study, all formulations are homogenously dispersed in a regular tetrahedron of coordinates (AM, CB, CNF, Binder). First, the microstructure was characterized in order to extract data related to the tortuosity, porosity and electrical conductivity. Then, galvanostatic measurements, in charge and discharge, from C/25 to 30C allowed to gather information such as the low- and high-rate capacity, energy and power and the Peukert constant for intermediate rates. The statistical analysis of all these results showed some clear correlation between the formulation and the microstructure and performance of the cells. Thus, it was made possible to draw the optimal formulation to get the best compromise depending on the application requirements (energy, rate or power). Finally, trends between the physical properties and performance were deciphered. This could be used to fine-tune the best possible electrode. Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".