Compatibilities of Conducting Polymer-Based Electrode Matrices for Lithium-Ion Batteries
Bibliographic record
Abstract
Aqueous electrode processing has been considered an important movement to make the overall battery fabrication process less toxic and more environmental-friendly 1 . The traditional approach focuses on mixing common aqueous binders such as carboxymethyl cellulose (CMC), polyacrylic acid (PAA), and styrene-butadiene rubber (SBR) with carbon additives to form conductive, water-processable electrode matrices 2 . This design, however, is unlikely to improve electrode interconnection, where carbon additives still loosely adhere to active materials 3 . Utilizing a better electrode matrix is believed to enhance both mechanical and electrical connections of electrode architecture, thus improving battery capacity and stability. Our recent work has demonstrated the design concept of conducting polymer composites as self-conductive and water-processable electrode matrices for Li-ion batteries 4 . By using in situ chemical polymerization method, polypyrrole:carboxymethyl-cellulose (PPy:CMC) composites, as a representative, were synthesized facilely and at low cost. The molecular structure of PPy:CMC composites composes of carboxyl groups from CMC as main anionic dopants for the positively charged PPy polymer chain. Their unique adhesion enables PPy:CMC composites to sufficiently connect active materials electrically and mechanically despite their low intrinsic electrical conductivity. As a result, cathodes with PPy:CMC composites and LiCoO 2 or LiNi 1/3 Mn 1/3 Co 1/3 O 2 showed promising cycling performance even at a 1-C rate, which is rarely reported for carbon-additive-free electrodes. Also, aqueous electrode casting is feasible for PPy:CMC-based electrodes, thus addressing toxicity and intensive-energy consumption associated with the current NMP-based electrode processing. Furthermore, their compatibility in Li-ion battery chemistry has also been tested employing SEM, EIS and XPS. This work shows initial efforts in designing a new kind of electrode matrix that enables a greener fabrication of Li-ion batteries. References J. Zhao, X. Yang, Y. Yao, Y. Gao, Y. Sui, B. Zou, H. Ehrenberg, G. Chen, and F. Du, Adv. Sci. , 5 , 1700768 (2018). D. Bresser, D. Buchholz, A. Moretti, A. Varzi, and S. Passerini, Energy Environ. Sci. , 11 , 3096–3127 (2018). V. A. Nguyen and C. Kuss, J. Electrochem. Soc. , 167 , 065501 (2020). V. A. Nguyen, J. Wang, and C. Kuss, J. Power Sources Adv. , 6 , 100033 (2020).
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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".