Lithium Polyacrylate (LiPAA) as an Advanced Binder and a Passivating Agent for High‐Voltage Li‐Ion Batteries
Bibliographic record
Abstract
Intensive studies of an advanced energy material are reported and lithium polyacrylate (LiPAA) is proven to be a surprisingly unique, multifunctional binder for high‐voltage Li‐ion batteries. The absence of effective passivation at the interface of high‐voltage cathodes in Li‐ion batteries may negatively affect their electrochemical performance, due to detrimental phenomena such as electrolyte solution oxidation and dissolution of transition metal cations. A strategy is introduced to build a stable cathode–electrolyte solution interphase for LiNi 0.5 Mn 1.5 O 4 (LNMO) spinel high‐voltage cathodes during the electrode fabrication process by simply using LiPAA as the cathode binder. LiPAA is a superb binder due to unique adhesion, cohesion, and wetting properties. It forms a uniform thin passivating film on LNMO and conducting carbon particles in composite cathodes and also compensates Li‐ion loss in full Li‐ion batteries by acting as an extra Li source. It is shown that these positive roles of LiPAA lead to a significant improvement in the electrochemical performance (e.g., cycle life, cell impedance, and rate capability) of LNMO/graphite battery prototypes, compared with that obtained using traditional polyvinylidene fluoride (PVdF) binder for LNMO cathodes. In addition, replacing PVdF with LiPAA binder for LNMO cathodes offers better adhesion, lower cost, and clear environmental advantages.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".