Aqueous Polypyrrole:Carboxymethyl Cellulose Conducting Binder for Graphite Electrodes in Lithium-Ion Batteries
Bibliographic record
Abstract
In lithium-ion batteries (LIBs), polymeric binders are crucial for maintaining the mechanical integrity of electrodes and ensuring continuous conductive pathways for both ions and electrons. The choice of binder material and its functional groups can significantly impact the battery's performance 1. Water-processable binders, in particular, offer both ecological and economic advantages. Among these, aqueous sodium carboxymethyl cellulose (CMC-Na) has shown promise in enhancing electrochemical performance compared to conventional binders, such as polyvinylidene fluoride (PVDF), especially when used in anodes 2. Unlike PVDF, CMC is biodegradable, water-soluble, and contains functional groups (–COOH and –OH) that form hydrogen bonds, improving its adhesion properties 3. Furthermore, CMC is cost-effective. However, CMC's intrinsic insulating nature limits its electron conductivity, which can hinder performance during cycling. This limitation can be addressed by using CMC as a dopant in polypyrrole (PPy), creating a conductive PPy:CMC composite with enhanced electrical conductivity, as well as thermal and environmental stability 4,5,6. In this study, we investigate the use of PPy:CMC composites as conducting binders in graphite anodes and evaluate the associated degradation mechanisms. Two types of graphite electrodes were fabricated: Graphite:PVDF:C and Graphite:PPy:CMC. To assess electrode stability and binder interactions with active materials and electrolytes, a range of characterization techniques were employed. Electrochemical stability and behavior were analyzed via galvanostatic cycling, cyclic voltammetry (CV), and impedance spectroscopy (EIS). Kinetic properties, lithium-ion transport, and interfacial resistance were also examined. Degradation products and mechanisms were investigated using X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDX). Electrode morphology and homogeneity were examined with scanning electron microscopy (SEM). The results provide valuable insights into the potential of PPy:CMC as a conducting binder in graphite anodes, laying the foundation for future research into high-energy-density anodes for LIBs and other energy storage applications. References 1. Shi, Y., Zhou, X. & Yu, G. Material and Structural Design of Novel Binder Systems for High-Energy, High-Power Lithium-Ion Batteries. Acc Chem Res 50, 2642-2652, doi:10.1021/acs.accounts.7b00402 (2017). 2. Song, J. et al. Interpenetrated Gel Polymer Binder for High-Performance Silicon Anodes in Lithium-ion Batteries. Advanced Functional Materials 24, 5904-5910, doi:10.1002/adfm.201401269 (2014). 3. Lingappan, N., Kong, L., Pecht, M. J. R. & Reviews, S. E. The significance of aqueous binders in lithium-ion batteries. 147, 111227 (2021). 4. Demirci, S., Sutekin, S. D. & Sahiner, N. Polymeric Composites Based on Carboxymethyl Cellulose Cryogel and Conductive Polymers: Synthesis and Characterization. Journal of Composites Science 4, doi:10.3390/jcs4020033 (2020). 5. Chou, S. L. et al. Tin/polypyrrole composite anode using sodium carboxymethyl cellulose binder for lithium-ion batteries. Dalton Trans 40, 12801-12807, doi:10.1039/c1dt10396b (2011). 6. Sasso, C. et al. Polypyrrole and polypyrrole/wood-derived materials conducting composites: a review. BioResources 6 (2011).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".