Microbial Fuel Cell (MFC)-Based in-Situ Water Toxicity Biosensor for Monitoring Heavy Metals and BTEX
Bibliographic record
Abstract
As the electricity is produced by exoelectrogenic bacteria in an microbial fuel cell (MFC), monitoring electrical signals from MFCs can provide a novel way of real-time health monitoring of many engineered systems and processes such as anaerobic digester systems. The purpose of this study was to investigate the potential of the MFC process as a water toxicity sensor for detecting toxins such as heavy metals and BTEX in the water. Cu<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup>, Hg<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup>, benzene, and xylene were selected as the representative toxicants and the toxicity response of the MFC biosensor was evaluated based on the inhibition ratio, indicative of the voltage changes when the toxicants were exposed to the anodic biofilm. It was found that the inhibition ratios were proportional to the concentrations of spiked toxicants. The toxicity responses toward heavy metals demonstrated an excellent linear relationship between the inhibition ratio and the toxicant concentration. Overall, this study demonstrated the potential of MFC technology as a water toxicity biosensor for real-time health monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".