Tuning the Particle Size Distribution at Cathode for Enhanced Li-Ion Battery Performance
Bibliographic record
Abstract
The Li-ion battery's diverse applications necessitate the quest to design energy and powerdense electrode microstructure.The present study uses the Doyle-Newman framework to probe the interplay of cathode particle size configurations for various current densities.Our study reveals the interplay of cathode particle size distribution and C-rate on cell performance characteristics.Further, the cell performs better when the cathode employs a cathode particle size configuration arranged non-uniformly.Moreover, the cell characteristics, viz.specific energy, specific power, and capacity, are highest for the configuration where the cathode particle size increases in the direction of the cathode current collector interface.Additionally, losses are relatively lesser in this configuration.Furthermore, the cell characteristics become more significant for higher current density.In addition, as particle size grows at a higher rate, the improvement in cell performance is significant.Our findings bear utility towards advancement in cathode on the microstructural scale and offer better spatiotemporal ionic transport kinetics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".