Comparative Evaluation of Coated and Non-Coated Carbon Electrodes in a Microbial Fuel Cell for Treatment of Municipal Sludge
Bibliographic record
Abstract
This study aims to provide insight into the cost-effective catalyst on power generation in a microbial fuel cell (MFC) for treatment of municipal sludge. Power production from MFCs with carbon, Fe2O3, and Pt electrodes were compared. The MFC with no coating on carbon generated the least power density (6.72 mW·m−2) while the MFC with Fe2O3-coating on carbon anodes and carbon cathodes generated a 78% higher power output (30.18 mW·m−2). The third MFC with Fe2O3-coated carbon anodes and Pt on carbon as the cathode catalyst generated the highest power density (73.16 mW·m−2) at room temperature. Although the power generated with a conventional Pt catalyst was more than two-fold higher than Fe2O3, this study suggests that Fe2O3 can be investigated further as an efficient, low-cost, and alternative catalyst of Pt, which can be optimized for improving performance of MFCs. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) results demonstrated reduced resistance of MFCs and better charge transfer between biofilm and electrodes containing coated anodes compared to non-coated anodes. Scanning electron microscopy (SEM) was used to analyze biofilm morphology and microbial community analysis was performed using 16S rRNA gene sequencing, which revealed the presence of known anaerobic fermenters and methanogens that may play a key role in energy generation in the MFCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".