Paradisiaca/Solanum Tuberosum Biowaste Composited with Graphene Oxide for Flexible Supercapacitor
Bibliographic record
Abstract
This work focuses on the development of a novel type of chemically activated carbon networks composited with the graphene oxide. Here, the carbon networks were derived from green biomass wastes such as orange peels, banana peels and potato starch. All the obtained carbon materials were being activated using different activating agents based on the higher specific capacitance such as phosphoric acid activated orange peel derived carbon, sulphuric acid activated banana peel derived carbon and hydrochloric acid activated potato starch. Further they were individually composited with graphene oxide for enhanced performance. Different chemical activation is employed for the sake of obtaining higher specific capacitance, energy and power density. Phosphoric acid activation on orange peel derived carbon network was selected due to the improvement in the micropores and further increased the surface area with the controlling capability of structures of activated carbon. To improve the conductivity of the samples, graphene oxide was added. The electrochemical performance of orange peel, banana peel and potato starch derived nano porous activated carbon materials composited with graphene oxide for supercapacitor applications is evaluated using aqueous H2SO4 electrolytes at a scan rate of 10 mV s-1. The samples that are prepared are structurally characterized using fourier transform infrared spectroscopy, x-ray diffraction and electrochemically characterized using cyclic voltammetry, galvanostatic charge and discharge measurements, and electrochemical impedance spectroscopy. From the electrochemical measurements, suitability of material as electrode for supercapacitors can be understood. The superior electrochemical performance is attributed in orange peel derived nano porous carbon/ graphene oxide due to porous structure.
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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".