Development of sustainable microbe-enhanced bio-carbon for supercapacitor applications
Bibliographic record
Abstract
Process engineering of biomass residues to develop cost-effective renewable nanomaterials for energy storage with high porosity, good ionic conductivity, and excellent stability is a necessary step toward a circular economy. In this study, we report a sustainable approach to creating bio-based nanoarchitecture from biofuel industry byproducts, i.e., microbe-treated groundnut shells. These carbon nanostructures were explored as electrode materials, demonstrating their potential as high-performance supercapacitors. A morphology investigation revealed that the microbial fortification of biomass acted as a natural porogen, leading to the formation of meso-nanopores with a high specific surface area as well as a high degree of graphitization, as validated by Raman spectroscopy. Galvanostatic charge-discharge curves exhibited quasi-triangular, symmetric shapes, confirming ideal capacitive behavior and high electrochemical reversibility. Electrochemical Impedance studies disclosed negligible IR drop and outstanding electronic conductivity with excellent capacitance performance due to the efficient distribution of electrolyte ions. Carbon nanostructures from spent substrates synthesized from groundnut shells displayed a high specific capacitance of 576 Fg -1 . This work offers a practical and evolutionary approach to advancing the development of biomass-based carbons for supercapacitor applications.
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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".