Binders for Li-Ion Battery Technologies and Beyond: A Comprehensive Review
Bibliographic record
Abstract
The effects of global warming highlight the urgent need for effective solutions to this problem. The electrification of society, which occurs through the widespread adoption of electric vehicles (EVs), is a critical strategy to combat climate change. Lithium-ion batteries (LIBs) are vital components of the global energy-storage market for EVs, and sodium-ion batteries (SIBs) have gained renewed interest owing to their potential for rapid growth. Improved safety and stability have also put solid-state batteries (SSBs) on the chart of top batteries in the world. This review examines three critical battery technologies: LIBs, SIBs, and SSBs. Although research has historically concentrated on heavier battery components, such as electrodes, to achieve high gravimetric density, binders, which comprise less than 5% of the battery weight, have demonstrated great promise for meeting the increasing need for energy storage. This review thoroughly examines various binders, focusing on their solubilities in water and organic solvents. Understanding binder mechanisms is crucial for developing binders that maintain strong adhesion to electrodes, even during volume fluctuations caused by lithiation and delithiation. Therefore, we investigated the different mechanisms associated with binders. This review also discusses failure mechanisms and innovative design strategies to improve the performance of binders, such as composite, conductive, and self-healing binders. By investigating these fields, we hope to develop energy storage technologies that are more dependable and efficient while also helping to satisfy future energy needs.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".