Conducting Polymer Composites As Multifunctional Electrode Matrices for Lithium-Ion Batteries
Bibliographic record
Abstract
The rising number of lithium-ion battery cells fabricated to power electric vehicles has raised concerns about the environmental impacts of battery electrode fabrication process. Typically, N-Methyl-2-pyrrolidone (NMP) solvent is used to blend active materials and electrode matrices, which are normally polyvinylidene fluoride/carbon-black (PVDF/C) mixtures. The use of NMP solvent, however, requires extensive energy for electrode drying and toxic solvent recovery. In addition, the PVDF/C electrode matrix offers weak interactions with active materials, which results in poor morphological integrity and rapid capacity fading, especially for high-volume-change electrode materials. Using water-processable binders such as carboxymethyl cellulose (CMC) and styrene-butadiene rubber (SBR) has been proven to enable a greener electrode-casting process as well as improve electrode performance. However, the electrical conductivity of these electrodes relies mainly on carbon additives, which are subjected to agglomeration and volumetric-capacity reduction. In this study, new electrode matrices are developed from in situ polymerized polypyrrole:carboxymethyl-cellulose (PPy:CMC) composites with water-processable and electrical-conductive features. By forming a composite structure in which CMC acts as an anionic dopant for PPy conducting polymer, the PPy:CMC composites show good electrical conductivity, allowing them to be used as mono-component electrode matrices. As a result, carbon-additive-free LiCoO2/PPy:CMC electrodes can cycle at different C-rate. More importantly, the PPy:CMC composites enable aqueous slurry electrode casting, addressing the environmental pollution associated with NMP solvent utilization. The study introduces another potential application of conducting polymer composites in Li-ion batteries. Keywords: aqueous electrode casting, conducting polymers, polypyrrole.
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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) | 0.001 | 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".