Investigating microbial interactions in dual chamber microbial fuel cells using a hybrid substrate of synthetic wastewater and human urine
Bibliographic record
Abstract
Microbial fuel cells (MFC) are emerging as a sustainable technology for wastewater treatment, utilizing microbial communities to transform waste substrates into valuable resources. The electrochemical behavior and dynamics of the microbial community inhabiting MFCs that utilize synthetic wastewater mixed with real human urine as anolyte have not been explored much. In this study electrochemical techniques, such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were harnessed to investigate the electrochemical performance and microbial population dynamics through 16 s metagenomic sequencing. Synthetic wastewater and varying dilutions of real human urine served as an anolyte, while acidic water(pH -2) as a catholyte across different batches of MFC. Three distinct batches were operated with anolytes that had urine concentrations of 20 %, 50 %, and 75 %, respectively. The CV demonstrated that when bacteria proliferate and biofilm develops gradually, the voltammograms region under the curve expands, a phenomenon that can be correlated to the anode's increasing capacitance. According to EIS assessments, the development of bacteria across the surface of the anode substantially reduces the impedance. The findings demonstrate that human urine contributes to essential ions and organic compounds, promoting microbial diversity and enhancing electron transfer processes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".