Investigation of Xanthan Gum and Carboxymethyl Cellulose Binders for the Silicon Anode of Lithium-Ion Batteries
Bibliographic record
Abstract
The binder is known to play an important role in the cycle stability of silicon-based anodes for lithium-ion batteries. Nature-derived biopolymers such as sodium carboxymethyl cellulose (NaCMC) and xanthan gum (XG) are a promising class of binders that offer several advantages over traditional polyvinylidene fluoride (PVDF). Advantages include better contact between silicon particles and the ability to process the electrodes using water as a solvent. While many studies have explored the fundamental properties of these biopolymer binders and their interaction with silicon, there has been little research on the use of these binders under practical loadings (such as ∼2 mg Si cm−2 and <10 wt% binder). Herein, we compare the electrochemical performance of both NaCMC and XG-based silicon electrodes with a low binder content. Si-binder interactions and their role in electrode performance are revealed with XPS, SEM, and EDX. In addition, we report the results of both a high-silicon (80 wt% Si) and a practical low-silicon (20 wt% Si) composite electrode while using silicon nano powder prepared by industrial-scale synthesis. It is found that NaCMC consistently outperforms XG as a binder, which is attributable to superior cohesion within the electrode.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".