(Invited) Improving Our Understanding of Conducting Polymer Binders
Bibliographic record
Abstract
As lithium-ion batteries reach their intrinsic performance limits, next-generation battery technology arises that relies much more strongly on the electrode matrix. Traditional compositions of binders and conductive additives with non-polar surfaces face intrinsic obstacles in these developments. In many next-generation electrode formulations, this incompatibility reveals itself through particle disconnection, increased electrode inhomogeneity1, and conductive additive agglomeration2. Conducting polymers are an apparent solution to such problems. Conducting polymers and their composites can be designed to exhibit increased adhesion, stronger interactions with active material surfaces and electrolytes, and favorable mechanical properties, while providing electronic connectivity at the adhesive contact with the active material3. Many examples have been presented in the literature that demonstrate improved electrode performance using such composites4–6. A common form of conducting polymer composites combines an intrinsically conductive polymer (e.g. polypyrrole, polythiophene, polyaniline) with a polyelectrolyte (e.g. polacrylate, polystyrene sulfonate, carboxymethyl cellulose). This presentation discusses an exploration of some of the fundamental properties of composites of polypyrrole with carboxymethyl cellulose. The work shows intrinsically favorable properties of the composite for application in batteries7. Among those properties are that it can be easily processed in water and N-methyl-2-pyrrolidone, exhibits better adhesion to the current collector, shows competitive conductivity and distributes evenly across active materials. However, it will also be shown that the native structure of this composite is not ideal for long-range conduction and that improvement can be made to the material’s conductivity and capacitance8. Our work is targeting a more complete understanding of the properties of these composites as they are employed in battery environments. It demonstrates enormous promise for the use of conducting polymers as binders, but also gaps in our understanding of their interactions with electrolytes and active materials that need to be addressed to advance their impact on the development of next-generation batteries. References: Müller, S. et al. Nat. Commun. 9, 2340 (2018). Scipioni, R. et al. J. Power Sources 307, 259–269 (2016). Nguyen, V. A. & Kuss, C. J. Electrochem. Soc. 167, 065501 (2020). Kim, J. M. et al. ACS Appl. Mater. Interfaces 6, 12789–12797 (2014). Salem, N., Lavrisa, M. & Abu-Lebdeh, Y. Energy Technol. 4, 331–340 (2016). Higgins, T. M. et al. ACS Nano 10, 3702–3713 (2016). Nguyen, V. A., Wang, J. & Kuss, C. J. Power Sources Adv. 6, 100033 (2020). Nguyen, V. A., Odetallah, M., Bakir, G., Gough, K. & Kuss, C. ACS Omega (2022).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.052 |
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".