Wearable flexible solid-state supercapacitors: Interface engineering using functionalized hexagonal boron nitride
Bibliographic record
Abstract
In pursuit of advanced energy storage systems for flexible electronics and sustainable energy applications, we report the development of highly cyclable, rechargeable, flexible solid-state supercapacitors via interface engineering with functionalized two-dimensional (2D) hexagonal boron nitride (hBN) nanoflakes. Functionalized hBN (Fh-BN) was integrated into all compartments of the supercapacitor, including the separator, flexible electrodes, and gel polymer electrolyte (GPE). The incorporation of Fh-BN into the carbon-based electrodes resulted in a 75 % enhancement in specific capacitance, reaching 350 F/g. Furthermore, the introduction of Fh-BN at the separator and GPE interfaces led to exceptional cycling stability, with over 80 % capacitance retention after 50,000 cycles, even under mechanical deformation. Fh-BN nanoflakes demonstrated excellent ion transport properties, facilitating efficient charge/discharge processes across all device components. This study highlights the crucial role of interface engineering in improving the performance of solid-state supercapacitors, offering a highly promising solution for energy storage in flexible electronics and wearable technologies. These results suggest a significant step forward in the design of next-generation energy storage devices with enhanced stability, flexibility, and efficiency. • Utilizing the Fh-BN in gel polymer electrolyte causes 6-time higher ion conduction. • Adding Fh-BN to the electrode formulation boosts charge transfer, enhancing capacitance by 75 %. • Presence of Fh-BN in all components of SC created interconnected pathways for ion movement. • The whole flexible supercapacitor showed sustainable flexibility with > 50,000 cyclability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".