Self-healable chitosan-based polymer binder for anode in lithium-ion batteries
Bibliographic record
Abstract
To enhance the performance and durability of lithium-ion batteries, the development of effective binder materials in the anode is crucial, with eco-friendliness being an additional consideration. This study presents a bio-based and self-healable polymeric binder designed for use in graphite-based anodes to achieve enhanced battery performance. The binder was synthesized via a Schiff base reaction between chitosan and a vanillin-derived linker, forming imine bonds that establish a self-healable 3D network. The bio-based binder effectively holds the graphite particles together while also providing excellent mechanical strength, thermal stability, and strong adhesion to the copper foil. Notably, it demonstrated self-healing behavior, with mechanical recovery exceeding 90 % after damage. Thermogravimetric analysis confirmed high thermal stability up to 300 °C, while the binder exhibited excellent tensile adhesion strength (ca. 21 kPa). Both coin and pouch cells fabricated with the bio-based binder achieved a specific capacity of 161.2 mAh g −1 and retained 81 % of their capacity after 250 cycles. Coulombic efficiency remained consistent at above 92 % even after 250 cycles, indicating no side reactions. Even at a high current density of 1.0C, the cells maintained approximately 50 % of their original capacity measured at 0.3C, showcasing excellent rate capability. Electrochemical impedance spectroscopy revealed low interfacial resistance, further validating the stability of the chitosan-based binder. We anticipate that this class of bio-based binders will not only extend the service life of lithium-ion batteries but also make a significant contribution to greener, safer energy storage technologies. • Chitosan-based binder for lithium-ion batteries: Developed a sustainable, bio-derived polymer binder as an alternative to conventional PVDF. • Enhanced electrochemical performance: Achieved improved capacity retention and cycling stability in graphite and/or anode electrodes. • Mechanical robustness: Demonstrated strong adhesion and flexibility, accommodating electrode volume changes during cycling. • Functional properties: Binder exhibited self-healing behavior, contributing to battery durability. • Environmentally friendly approach: Utilizes a biodegradable, renewable material, supporting greener battery manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".