Hierarchically Structured Nitrogen-Doped Multilayer Reduced Graphene Oxide for Flexible Intercalated Supercapacitor Electrodes
Bibliographic record
Abstract
Intercalated flexible electrodes for energy storage devices have drawn significant research interests as they can provide high energy densities for powering electronics without sacrificing the overall flexibility. Herein, we report an intercalated reduced graphene oxide/polyacrylonitrile (rGO/PAN) flexible supercapacitor electrode fabricated via a layer-by-layer wet electrospinning (LLwES) process with diluted graphene oxide (GO) solution as the coagulation liquid and subsequent thermal reduction treatment. It was observed that a thin GO film was established on individual PAN nanofiber layer after the wet electrospinning process, while the subsequent thermal reduction of GO led to simultaneous stabilization of the PAN fibers and the creation of an interesting three-dimensional hierarchical carbon nanostructure suitable for flexible, high-performance electrochemical capacitor (EC) electrodes. The formation of gases during the thermal treatment expanded the electrospun PAN fiber layers and resulted in the formation of intercalated nitrogen-doped porosities. The resulting LLwES rGO/PAN system, thermally treated in a nitrogen atmosphere, demonstrated exceptional double-layer capacitance of 221 F/g at 10 mV/s, a controllable electrical conductivity of 125 S/m, and a stable cycling performance retaining a slightly increased capacitance after 10000 cycles.
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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) | 0.000 | 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".