Renewable Biopolymers Combined with Ionic Liquids for the Next Generation of Supercapacitor Materials
Bibliographic record
Abstract
The search for biocompatible and renewable materials for the next generation of energy devices has led to increasing interest in using biopolymers as a matrix component for the development of electric double-layer capacitors (EDLCs). However, using biopolymers as host matrices presents limitations in performance and scalability. At the same time, ionic liquids (ILs) have shown exceptional properties as non-aqueous electrolytes. This review intends to highlight the progress in integrating ILs and biopolymers for EDLC. While ILs have been used as solvents to process biopolymers and electrolyte materials, biopolymers have been utilized to provide novel chemistries of electrolyte materials via one of the following scenarios: (1) acting as host polymeric matrices for IL-support, (2) performing as polymeric fillers, and (3) serving as backbone polymer substrates for synthetic polymer grafting. Each of these scenarios is discussed in detail and supported with several examples. The use of biopolymers as electrode materials is another topic covered in this review, where biopolymers are used as a source of carbon or as a flexible support for conductive materials. This review also highlights current challenges in materials development, including improvements in robustness and conductivity, and proper dispersion and compatibility of biopolymeric and synthetic polymeric matrices for proper interface bonding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".