Improvement of Microbial Fuel Cell Performance by Selection of Anodic Materials and Enrichment of Inoculum
Bibliographic record
Abstract
This work aimed at evaluating the effect of four anodic materials and the use of enriched inocula on the microbial fuel cell (MFC) performance. The anodic materials were granular activated carbon (GAC), graphite rod (GR), triangles of graphite (GT) and graphite flakes (GF). When loaded with a sulfate-reducing inoculum (SR-In) the internal resistance (Rint) obtained were 273, 410 and 795 Ω for GF, GT, GR, respectively and higher than 10 000 Ω for GAC, whereas the maximum volumetric power (PV,max) were 1326, 2108 and 3052 mW m-3 for GR, GT and GF, respectively. We observed a decrease of Rint and an increase of PV,max with the increase of the log of A´s of the graphite anodic materials that was consistent with a mathematical model previously reported by our Group. The use of the Fe (III)-reducing inoculum significantly enhanced the MFC performance; PV,max was up to 5000 mW m-3, 40% higher than the power obtained with SR-In whereas the Rint was 140 ohms. Highest PVs of our MFC were close to values of electricity power derived from the anaerobic digestion of municipal wastewaters. In this regard, results of this work point out to a promising approach to further tapping bioelectricity from organic wastes that previously have yielded biohydrogen.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".