Electricity generation and oxidoreductase potential during dye discoloration by laccase-producing Ganoderma gibbosum in fungal fuel cell
Bibliographic record
Abstract
Abstract Color chemicals contaminate pure water constantly discharged from different points and non-point sources. Physical and chemical techniques have certain limitations and complexities for bioenergy production, which motivated the search for a novel sustainable production approaches during dye wastewater treatment. The emerging environmental problem of dye decolorization has attracted scientist's attention to a new, cheap, and economical way to treat dye wastewater and power production via fungal fuel cells. Ganoderma gibbosum was fitted in the cathodic region with laccase secretion in the fuel cell. At the same time, dye water was placed in the anodic region to move electrons and produce power. This study treated wastewater using the oxidoreductase enzymes released extracellularly from Ganoderma gibbosum for dye Remazol Brilliant Blue R (RBBR) degradation via fungal-based fuel cell. The maximum power density of 14.18 mW/m2 and the maximum current density of 35 mA/m2 were shown by the concentration of 5 ppm during maximum laccase activity and decolorization of RBBR. The laccase catalysts have gained considerable attention because of eco-friendly and alternative easy handling approaches to chemical methods. Fungal Fuel Cells (FFCs) are efficiently used in dye treatment and electricity production. This article also highlighted the construction of fungal catalytic cells and the enzymatic performance of fungal species in energy production during dye water treatment.
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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".