Long-Term Performance of Electrodes Based on Vinyl Acetate Homo-Polymer Binder
Bibliographic record
Abstract
In this work we propose the use of a hydro-dispersible polymer such as the poly vinyl acetate as a binder for the production of electrodes for lithium-ion batteries. To increase the film forming properties of the polymer the poly vinyl was added with triacetin that acts as a plasticizer. The electrochemical stability of the polymer was tested by a polarizing electrode, formed by mixing the polymer with carbon. Subsequently, an electrode tape was prepared by using LiNi0.5Mn1.5O4 as the active material and characterized by SEM, EDS and TGA. Lithium metal cells were assembled and tested to evaluate specific capacity, power and energy density at various discharge rates. The cycle life of the cell was evaluated by galvanostatic charge/discharge cycles. The tests showed that the electrodes prepared with PVA plasticized with triacetin have very good electrochemical performance in terms of capacity retention as a function of the discharge rate and the cycle number. Our work demonstrates that the use of triacetin to plasticize the PVA allows to increase the electrochemical stability of the electrode likely due to an improvement of the slurry filmability. The proposed method could represent a promising technology for the production of long-term performance lithium batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".