Microbial fuel cell power overshoot studied with microfluidics: from quantification to elimination
Bibliographic record
Abstract
Power overshoot can hinder determination of maximum power densities in microbial fuel cells (MFCs). In this work, a microfluidic approach was used to study overshoot in an MFC containing a pure culture of electroactive biofilms (EAB) containing Geobacter sulfurreducens. After 1-month operation under constant flow of an ideal nutrient medium, the MFC health began to degrade, marked by voltage loss and the appearance of anomalies in the power density curves. One such anomaly was a chronic power overshoot, accompanying a loss of both measured power and current density on the high-current side of the power density curve. The degree of power overshoot was quantified while certain flow-based interventions were applied, notably the shear erosion of the EAB outer layer. Next, two approaches to acclimation were demonstrated to treat the remaining overshoot. The standard approach, which acclimates the MFC to high currents before a standard polarization test, eliminated the remaining overshoot and returned maximum power densities to initial levels, but maximum current density remained lower than the initial level. A microfluidic-assisted “long-hold polarization test” enabled efficient in situ acclimation of each external resistor during the measurement. Despite the health-compromised MFC, this method provided long-term stability during the polarization test, resulting in power and current density measurements that exceeded those made on the healthy MFC using the standard polarization test. We conclude that slower electron transfer kinetics in unhealthy MFCs can provoke overshoot by prolonging the time to reach steady state during the polarization test, but a properly designed measurement overcomes this problem.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".