Impact of binder content on particle fracture and microstructure of solvent-free electrodes for Li-ion batteries
Bibliographic record
Abstract
The fraction of polytetrafluoroethylene (PTFE) binder in solvent-free Li-ion battery electrodes is shown to have a dramatic impact on their processability, microstructural evolution and elec- trochemical performance. We show experimentally that increasing binder fraction from 0.5 to 4 wt.% transformed the electrode microstructure from an efficient, open structure containing PTFE nano-fibrils to a cemented morphology with blocked porosity. The solvent-free electrodes showed a classical visco-elastic response during compression, comprising three distinct regions of deformation. The electrode stiffness and yield/flow strength increased non-linearly with binder fraction such that for higher binder contents (>2wt.%), there was extensive LiNi0.6Co0.2Mn0.2O2 (NMC) particle fracture during the calendering process, with cracks propagating along the grains of polycrystalline NMC particles. Conversely at lower binder fraction (<2wt.%), the PTFE readily fibrillated into highly textured (100) crystalline nano-fibrils and NMC particles remained largely intact. These electrodes showed superior electrochemical performance due to higher ionic mobility through the open nano-fibrillar microstructure and intact NMC particles.
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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 | 0.000 |
| 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".