The Influence of Different Electrode Fabrication Methods and Poly(Vinylidene Fluoride) Binders on The Anode Electrode Dimension Stability and Cyclability in Lithium-Ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries, composed of two electrodes, are familiar energy-storage systems and have been studied in detail by many research groups over the past years. Both of the anode and cathode in lithium-ion batteries are composed of electrochemically active materials. A polymeric binder is needed to maintain the physical integrity of the coating and its adhesion to substrate. Various polymers, including poly(vinylidene fluoride) (PVDF) have been considered as host polymers for polymer electrolytes. This study investigated the influence of the fabrication methods, including drying and annealing steps at different oven temperatures, on the anode electrode dimension swelling ratio (AEDSR) in lithium-ion batteries as well as the cyclability of the overall cells. It was shown that electrodes annealed at 160 degrees C have a higher AEDSR than those annealed at 90 degrees C because of recrystallization of PVDF during annealing at the higher temperature. Similarly, electrodes annealed at 160 degrees C retain a higher value of AEDSR. However, the AEDSR of fully-charged electrodes annealed at 90 degrees C is greater than that of electrodes annealed at 160 degrees C. It was concluded that the different types of binders influence the AEDSR as well as the cyclability, both at room temperature and at 45 degrees C. 18 refs., 1 tab., 7 figs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".