Ionically‐Functionalized Poly(thiophene) Conductive Polymers as Binders for Silicon and Graphite Anodes for Li‐Ion Batteries
Bibliographic record
Abstract
Abstract Next‐generation anode materials for Li‐ion batteries such as silicon can lead to ten times more capacity than the state‐of‐the‐art graphite. However, novel binders are required to overcome the detrimental effects of volume changes during battery cycling of silicon because of the poor chemical interaction and electrical conductivity between silicon and the binder. Most studies focus on either ionic binders or electrically conductive binders, but herein it was demonstrated that a new family of polymers based on electrically conductive poly(thiophene) functionalized with an ionic alkyl carboxylate groups of various lengths can successfully work as multifunctional binders for silicon and commercial graphite anodes in Li‐ion battery half‐cells. It was determined that the polymer with shorter side chain (PT‐3‐LiA) gives the highest reversible capacity upon pairing with graphite or silicon, reaching 3000 mAh g−1 in the case of the latter, a capacity 500 mAh g−1 (≈22 %) higher than those obtained with the electrically, but non‐ionically, conductive PEDOT:PSS binder and the ionically, but non‐electrically, conductive sodium carboxymethyl cellulose (NaCMC) binder. It is demonstrated that the superior performance of this new type of multifunctional binders can be attributed to their ability to maintain their doping level and conductivity as well as due to good interaction with the silicon surface during cycling.
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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".