Review—Conducting Polymer-Based Binders for Lithium-Ion Batteries and Beyond
Bibliographic record
Abstract
In the search for active Lithium-ion battery materials with ever-increasing energy density, the limits of conventional auxiliary materials, such as binders and conducting additives are being tested. Binders adhere to active substances and current collectors, yielding an interconnected electrode structure that ensures mechanical integrity during the (de-)lithiation process. Even though the battery binder only accounts for a fraction of battery weight and cost, it is a bottleneck technology in the deployment of high energy density active materials that experience significant volume variation and side-reactions. This review paper discusses research on alternative binders derived from conducting polymers (CPs). The use of CPs in binders enables mechanically flexible electronic contacts with the active material with the goal of accommodating larger volume changes within the electrode. Following a summary of the reasoning behind the use of CP-based binders, their rational design is reviewed, including novel composite syntheses and chemical modifications. A new class of multifunctional CP-based binders exhibits promising properties such as high electronic conductivity, the ability for aqueous processing, and efficient binding that tackle the limiting features of traditional binders. The practical application of these binders in Li-ion batteries and beyond is summarized, yielding an outline of current achievements, and a discussion of remaining knowledge gaps and possible future development of such binders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".