Flexible Supercapacitors Based on Polyaniline Arrays Coated Graphene Aerogel Electrodes
Bibliographic record
Abstract
Flexible supercapacitors(SCs) made by reduced graphene oxide (rGO)-based aerogel usually suffer from the low energy density, short cycle life and bad flexibility. In this study, a new, synthetic strategy was developed for enhancing the electrochemical performances of rGO aerogel-based supercapacitor via electrodeposition polyaniline arrays on the prepared ultralight rGO aerogel. The novel hybrid composites with coated polyaniline (PANI) arrays growing on the rGO surface can take full advantage of the rich open-pore and excellent conductivity of the crosslinking framework structure of 3D rGO aerogel and high capacitance contribution from the PANI. The obtained hybrid composites exhibit excellent electrochemical performance with a specific capacitance of 432 F g -1 at the current density of 1 A g -1 , robust cycling stability to maintain 85% after 10,000 charge/discharge cycles and high energy density of 25 W h kg -1 . Furthermore, the flexible all-solid-state supercapacitor have superior flexibility and outstanding stability under different bending states from the straight state to the 90° status. The high-performance flexible all-solid-state SCs together with the lighting tests demonstrate it possible for applications in portable electronics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".