Soft-assembled, bio-gel electrolytic double layer capacitor system for sustainable energy storage
Bibliographic record
Abstract
Carrageenan is an attractive bio-derived polymer electrolyte due to its high ionic conductivity, cyclic stability, and cost-effectiveness in copolymerizing with other biocompatible polymers such as polyvinyl alcohol (PVA). This paper discusses the physical and chemical characterizations of the bio-electrolyte(s): PVA, carrageenan, and PVA-carrageenan copolymer blend. We also describe a fabrication method to create a soft-printed, thin-film, bio-gel electrolytic double-layer capacitor (bio-EDLC) using a PVA-carrageenan polymer blend. The addition of PVA to carrageenan provided structural and mechanical stability to the biopolymer, as confirmed by the hydrogen bonds detected using attenuated Fourier-transform infrared spectroscopy analysis. The assembled bio-EDLCs were analyzed for their electrical properties including their internal charge transfer mechanism, electrical breakdown, specific capacitance, power density, and energy density. It was determined that the bio-EDLC offers specific capacitance of 84 F/g and power density of 4.15E-02 W/kg and has a breakdown voltage of ~1.1 V. Furthermore, preliminary optimization was performed on the bio-EDLC with a drop casting of reduced graphene oxide at the electrode/electrolyte interface to create surface micro-roughness resulting in a bio-EDLC system which demonstrated superior electrical characteristics with an overall specific capacitance of up to 118 F/g, breakdown voltage of ~1.3 V, and improved maximum charging capability to 80%.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".