Flexible and Self-Healing Aqueous Supercapacitors for Low Temperature Applications: Polyampholyte Gel Electrolytes with Biochar Electrodes
Bibliographic record
Abstract
A flexible and self-healing supercapacitor with high energy density in low temperature operation was fabricated using a combination of biochar-based composite electrodes and a polyampholyte hydrogel electrolyte. Polyampholytes, a novel class of tough hydrogel, provide self-healing ability and mechanical flexibility, as well as low temperature operation for the aqueous electrolyte. Biochar is a carbon material produced from the low-temperature pyrolysis of biological wastes; the incorporation of reduced graphene oxide conferred mechanical integrity and electrical conductivity and hence the electrodes are called biochar-reduced-graphene-oxide (BC-RGO) electrodes. The fabricated supercapacitor showed high energy density of 30 Wh/kg with ~90% capacitance retention after 5000 charge-discharge cycles at room temperature at a power density of 50 W/kg. At -30 °C, the supercapacitor exhibited an energy density of 10.5 Wh/kg at a power density of 500 W/kg. The mechanism of the low-temperature performance excellence is likely to be associated with the concept of non-freezable water near the hydrophilic polymer chains, which can motivate future researches on the phase behaviour of water near polyampholyte chains. We conclude that the combination of the BC-RGO electrode and the polyampholyte hydrogel electrolyte is promising for supercapacitors for flexible electronics and for low temperature environments.
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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) | 0.000 | 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 | 0.000 | 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".