Nanoarchitectonics approach to graphite/starch-supported bioelectrode for enhanced supercapacitor performance
Bibliographic record
Abstract
There has been an increasing interest in finding suitable materials for supercapacitor applications in response to the growing need for energy, to use alternative energy sources to fossil fuels in addition to energy storage. In this regard, bio-based carbon-loaded materials can be a promising option for high-performance supercapacitors because of their abundance, diversity, and reproducibility with waste management strategies. In this study, a new graphite-loaded bioelectrode is synthesized for supercapacitor application. The electrochemical performance of the synthesized electrode is tested at room temperature using the cyclic voltammetry method, and the capacity and energy density of the electrodes are evaluated. The electrochemical performance of 1 g of graphite-loaded bioelectrode was 3.5 mA/cm 2 , while the specific capacitance value was 355.6 F/g at a current density of 0.5 A/g. Furthermore, the bioelectrode provided significant cyclic stability with 93.5% in specific capacitance value after 5000 charge/discharge cycles at the current density of 0.5 A/g. Consequently, the synthesized bioelectrode can be a promising option for energy storage as a sustainable electrode due to its superior conductivity, stability, and low cost. • Graphite-doped starch-derived bioelectrode was prepared for supercapacitor applications. • Starch-derived graphite-loaded bioelectrode displayed high carbon content of 77.7 %. • Highest capacitance value was 355.6 F/g at 0.5 A/g for 1 g of graphite-loaded bioelectrode. • Bioelectrode had a high rate cycling capability of 93.5 % at 0.5 A/g. • The assembled supercapacitors presented notable capacitance at high graphite loadings.
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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 | 0.001 | 0.001 |
| 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".