Chemically Treated Activated Carbon for Supercapacitor Electrode Derived from Starch of Solanum Tuberosum
Bibliographic record
Abstract
Because of the increasing demands in energy, growth in alternate energy sources is inevitable. By activation followed by carbonization of starch of solanum tuberosum makes a porous carbon network as electrode material for supercapacitor. To achieve an improvement in specific capacitance, activating the starch of solanum tuberosum using chemicals such as hydrochloric acid, phosphoric acid and sulphuric acid were done. The structural and electrochemical performance of activated carbon derived from the starch of solanum tuberosum is evaluated using FTIR, XRD, CV, GCD and EIS. The improved performance of chemically treated starch of solanum tuberosum in energy storage mechanism is ascribed to fast ionic diffusion of the electrolyte into and out of the pores. From the CV analysis, the higher specific capacitance (94 F.g-1) is obtained for sulphuric acid treated activated carbon derived from the starch of solanum tuberosum with good capacity retention ratio. From GCD analysis, 78.3, 56.7, 82.7 and 74.9 Fg-1 of specific capacitance is obtained for POC, POCH, POCP and POCS, respectively. When compared to all the prepared samples, POC exhibited small equivalent series resistance and POCS exhibited small charge transfer resistance.
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 | 0.000 |
| 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".