Flexible Solid-State Supercapacitor Using 2D White Graphene
Bibliographic record
Abstract
Future inventions and technologies like miniaturize electronics with various functionalities, wearable devices, and bio-implantable sensors have demand for reliable and efficient flexible energy storage devices that can stand mechanical deformation. The flexibility of energy storage devices depends upon configuration, and assembly design to integrate all components into a single flexible unit. In this work, we exhibit a flexible all solid-state supercapacitor which consists of two flexible electrodes, fabricated by modified activated carbon with 2D nanostructure of hexagonal boron nitride (h-BN) ionomers. The electrodes are separated by a nano-engineered electrolyte membrane developed in our previous work. The surface-functionalized h-BN (FhBN) ionomers and nanocomposite electrolyte membranes showed superionic conduction with through- and in-plane ion conductivity of 0.1 S.cm−1 and 0.41 S.cm−1, respectively, which are 14 and 7 times higher than the commercial Nafion ionomer and membrane. The performance of the flexible supercapacitor was validated by cyclic voltammetry, constant current charge/discharge tests, and electrochemical impedance spectroscopy (EIS). These tests are performed in symmetric assembly of supercapacitor and displayed outstanding areal-specific capacitance which was ~2 times higher than that of a supercapacitor made of commercial Nafion electrolyte. Furthermore, we observed that the FhBN-based flexible supercapacitor can conserve its capacitance during the bending test under the mechanical stress, demonstrating its outstanding mechanical flexibility while maintaining its electrochemical performance. With the exceptional mechanical and electrical robustness, the FhBN-based flexible supercapacitors promises an exciting candidate for high-performance flexible energy storage devices in wearable electronics.
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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".