Solvent‐Free Manufacturing of Lithium Iron Phosphate Cathodes via Binder Fibrillation for Li‐Ion Batteries
Bibliographic record
Abstract
Solvent‐free electrode manufacturing reduces cost and carbon emissions in Li‐ion battery production via eliminating the electrode drying and toxic solvent recovery processes in conventional manufacturing. Herein, lithium iron phosphate‐based cathodes are solvent‐free manufactured via binder fibrillation. Effects of electrode thickness, porosity, and composition on cell performance are investigated for electrode optimization. By balancing the electrode thickness and porosity, the areal capacity exhibits a collective increase under different C‐rates, such as 2.0–2.6 mAh cm −2 uplifting from 0.1 to 0.75 C. These results are different from increasing electrode thickness alone where the increasing tendency in areal capacity becomes flattened with C‐rate, indicating the reduction of active material unitization in thick electrodes. Adhesive lamination of electrode sheets on current collectors is found to improve C‐rate and cycling performance in comparison to non‐laminated electrodes, and the improvement is more effective at higher C‐rates. Four‐probe electrode sheet resistance tests reveal that the resistivity of dry electrodes is independent of electrode thickness when the composition is fixed, which indicates the uniformity and reproducibility the dry electrodes. By increasing the carbon to binder ratios while keeping the active material fraction the same, the electrode resistivity is decreased by 30% which leads to the improvement of C‐rate performance.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".