Polypyrrole:Carboxymethyl Cellulose Composites As Efficient, Multifunctional Electrode Matrices for Lithium Battery Cathodes
Bibliographic record
Abstract
Even though the conventional electrode matrix PVDF/C-black is widely used in lithium batteries to provide adhesion and an electronically conducting matrix for active electrode materials, its performance as a binder is not sufficient for many high capacity materials, such as silicon and Lithium Nickel Manganese Cobalt Oxide. The problem is mainly attributed to the weak interactions between inert PVDF binder and non-polar carbon additives with active materials. As a result, PVDF/C cannot buffer volume changes of active materials during lithiation and delithiation that induce electrode cracking and side-reactions, leading to electrical-contact loss and capacity fading. To overcome these problems, several studies suggest the use of a multifunctional electrode matrix of conducting polymer composite PEDOT:PSS. However, the production cost of PEDOT:PSS prohibits its wide-scale application in battery technologies. In this research, we proposed a facile, scalable, low-cost in-situ polymerization method for an analogous polymer composite. We are demonstrating the fabrication of highly conductive, water-processable polypyrrole:carboxymethyl cellulose (PPy:CMC) composite as a new class of electrode matrix. The PPy:CMC (1:1 wt%) is formed at nano-level with a particle size of approximately 50 nm. The electrical conductivity of PPy:CMC (1:1 wt%) was found to be 144 mS/cm, which was smaller than that of pure PPy at approximately 1.73 S/cm, however was 10 times higher than that of ball-milled PPy:CMC (1:1 wt%) composite (mS/cm). This could be interpreted as the formation of a molecular-level composite, with ionic interactions between the positively charged polypyrrole and the negatively charged carboxyl groups on CMC. The presence of CMC in the composite structure also resulted in the ability to disperse the composite in water, which allows environmental friendly electrode processing. Thanks to the self-conductive feature of PPy:CMC composite, no additional conductive or binder additive is needed in the electrode. More importantly, we are targeting to reduce capacity degradation by improving adhesion between the conductive binder to active material particles. This study highlights how research into inactive materials can provide alternative inexpensive electrode matrices, widening the property space within which new active materials can be expected to operate. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".