Ionomer Binders Can Improve Discharge Rate Capability in Lithium-Ion Battery Cathodes
Bibliographic record
Abstract
A lithium-ion form of a perfluorosulfonate ionomer was used as a binder in Li Fe P O 4 -based lithium-ion battery cathodes. Carbon-coated Li Fe P O 4 and acetylene carbon black were blended with ionomer to prepare composite cathodes having a composition 60% Li Fe P O 4 , 20% acetylene carbon black, and 20% binder by weight. Cathodes were tested against Li 4 Ti 5 O 12 anodes using 1.0 M and 0.1 M Li P F 6 –ethylene carbonate/diethyl carbonate (EC/DEC) electrolytes. Comparison was made with cathodes prepared using poly(vinylidene) difluoride (PVDF) as binder. At low discharge rates (e.g., C/5) both cathode types exhibited similar charge–discharge capacities and voltage profiles. However, under higher rate discharge conditions (e.g., 1 C , up to 5C) cathodes prepared using ionomer binder showed better discharge rate capability than cathodes having PVDF binder. This phenomenon was more pronounced when the salt concentration in the electrolyte was low (e.g., 0.1 M Li P F 6 – E C ∕ D E C ). These findings suggest that use of ionic binders can help to compensate for electrolyte depletion from the electrode porous space as lithium ions are intercalated into lithium-deficient Li Fe P O 4 particles during rapid discharging. Potential uses for electrodes having ionomer binders in enabling lower cost battery electrolytes (because of the reduced need for salt) and in developing high rate cathodes that are nonporous or have low porosity are discussed.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".