Mechanical and electrical properties of poly(vinylidene fluoride–tetrafluoroethylene–propylene)/Super‐S carbon black swelled in liquid solvent as an electrode binder for lithium‐ion batteries
Bibliographic record
Abstract
Abstract The mechanical and electrical properties of poly(vinylidene fluoride–tetrafluoroethylene–propylene) (PVDF–TFE–P) and carbon black–filled PVDF–TFE–P composites were investigated. The carbon black was used for its electrolyte absorption properties in addition to boosting the conductivity. This elastomeric binder system may have application to tin‐ or silicon‐based electrode materials for Li‐ion batteries, which undergo huge volumetric changes during charge/discharge cycling. The mechanical and electrical properties were measured while film samples were immersed in a liquid solvent (ethylene carbonate : diethyl carbonate 1 : 2) commonly used in the battery electrolyte. Uncrosslinked PVDF–TFE–P uptakes about 140% solvent by mass and swells significantly. The amount of solvent absorbed can be reduced and the mechanical properties improved by crosslinking the polymer. Two crosslinking recipes, based on bisphenol and triethylenetetramine (TETA), were investigated carefully. Compared to the bisphenol‐based crosslinking recipe, the proposed TETA‐based crosslinking recipe gave films with a higher degree of crosslinking and better mechanical properties. The TETA‐crosslinked composites had very good mechanical and electrical reversibility even during cyclic deformation to 100% strain. The cycling results of amorphous Si0.64Sn0.36 electrodes show that the capacity retention of the electrodes can be significantly improved by using the proposed elastomeric binder. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 2958–2965, 2004
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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 | 0.000 | 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".