3D-printed single-chamber microbial fuel cell biosensor reliably measures nitrates in environmental waters
Bibliographic record
Abstract
This study introduces a novel single-chamber microbial fuel cell (MFC) design employing various anode materials (carbon felt, carbon cloth, and Toray carbon paper) to detect nitrate in synthetic wastewater and environmental samples. Key parameters, including organic matter concentration (ranging from 100 to 1000 mg/L sodium acetate (NaAc)), external resistance (240–1000 Ω), and feeding flow rate (3.5 and 8.5 mL/min), were investigated to understand their impact on the MFC's performance. The optimised parameters were identified as 300 mg/L NaAc concentration, 240 Ω external resistance, and 3.5 mL/min flow rate. The MFCs were calibrated using synthetic wastewater with nitrate concentrations of 0 (blank sample), 0.5, 1, 2, 2.5, and 5 mg/L, demonstrating high sensitivity and a robust logarithmic correlation (R² = 0.92) within this range. The investigation was extended to real environmental water samples collected from four distinct rivers in Melbourne, Australia to validate the lab results. The optimised MFC biosensor successfully detected nitrate concentrations as low as 0.18 mg/L in these samples. This performance highlights the biosensor's potential for real-world water quality monitoring. To enhance the practicality of this technology for field-scale applications, such as early warning systems for detecting illicit discharges in natural water bodies, future work is needed. Key challenges of this technology include reducing the exposure of oxygen to the microbial community and maintaining a constant organic matter background concentration. • Introducing cost-effective, open source MFC biosensors using 3D-printing. • Optimised MFC biosensor detected nitrate as low as 0.18 mg/L in environmental waters. • MFC showed promise for simultaneous detection of nitrate and organic matter. • Validation of reproducibility of MFC biosensors for nitrate trend detection.
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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) | 0.001 | 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 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".