Crosslinked Chitosan Networks as Binders for Silicon/Graphite Composite Electrodes in Li-Ion Batteries
Bibliographic record
Abstract
Silicon/graphite composites have the potential to improve the practical energy density of Li-ion batteries to enable mass-market penetration of electric vehicles. However, they require polymeric binders that are compatible with both silicon and graphite and can sustain alloying and intercalation reactions as well as the associated interfacial reactions. In this work, chitosan, a natural cellulose, is crosslinked with either molecular (citric acid) or polymeric (poly (acrylic acid), PAA) carboxylic acids to form networks with maximum interaction with the composite as evidenced by infrared spectroscopy. Li-ion half-cells of graphite-rich, silicon/graphite negative electrodes using crosslinked chitosan binders show higher initial Coulombic efficiency (ICE) and more stable cycling performance than pristine chitosan. This could be due to the polymeric network produced from the crosslinking reaction between chitosan and carboxyl acids as well as the strong interactions between polymeric network and surface of silicon and graphite as evidenced by adhesion tests. The crosslinked chitosan network can effectively accommodate large volume change of silicon particles and keep other electrode components connected during cycling as evidenced by scanning electron microscope (SEM) images leading to excellent cycling stability. This makes it very attractive for use as a binder in Si/graphite electrodes for Li-ion batteries.
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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 | 0.000 | 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) | 0.001 | 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".