Performance evaluation of microbial fuel cell using novel anode design and with low-cost components
Bibliographic record
Abstract
Microbial fuel cells (MFCs) have proven to be an effective technology for treatment of waste water with the additional advantage of electricity generation. Although the power density obtained has increased manyfold over the past decade, the cost of treatment and cost of electricity generation need to be brought down to make the process feasible. In the present research, an attempt was made to use locally available, low-cost and effective materials for the construction of an MFC using novel anode architecture. The MFC was made using multiple membranes in a single cell. The special design of the anode proved to be very effective in obtaining a higher power density. A volumetric power density of 2002 mW/m3 could be achieved without the use of any chemical catholyte. The corresponding coulombic efficiency obtained was 13.17%. When a chemical catholyte was used, the power density increased to 5201 mW/m3, an increase by more than 2.5 times. The corresponding coulombic efficiency of the MFC also increased to 29.16%. Such novel anode architecture could take this technology a step forward for practical implementation to harvest carbon dioxide neutral electricity from waste water. The performance of the MFC in the removal of chemical oxygen demand (COD) from waste water was found to be 93.9–97.75%, which is highly satisfactory. The removal efficiency was found to be independent of the initial COD of the substrate.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".