Multiparameter optimization of microbial fuel cell outputs using linear sweep voltammetry and microfluidics
Bibliographic record
Abstract
A microbial fuel cell with a mature pure-culture Geobacter sulfurreducens electroactive biofilm was used for performance optimization by making rapid changes to experimental parameters in microchannels while monitoring their effect using linear sweep voltammetry. A systematic investigation of polarization behavior and evaluation of system resistivity provided important figures of merit and mechanistic insights on the effects of flow rates, concentrations, and temperature. After individual parameters were optimized, a synergistic effect was observed by applying optimal parameters together, resulting in improved current and maximum power densities, compared to stable values at unoptimized conditions. Continued acclimation for just 2 days under these conditions resulted in further improvements to anode area-normalized current and power maxima (10.49 ± 0.23 A m −2 and 2.48 ± 0.27 W m −2 ), which are among the highest reported in the literature for a microfluidic MFC. In keeping with other accepted normalization protocol using the area separating anode and cathode chambers, the output densities were 64 A m −2 and 15 W m −2 , which are near the theoretical maximum outputs achievable with an MFC system. • Operational parameters were rapidly and accurately modified with microfluidic control. • Linear scan voltammetry reported power and current outputs plus internal resistances. • Individual optimized operational parameters were determined. • Optimization reduced internal resistances and delayed mass-transport limitations. • Maximum outputs were 15 W m −2 and 63 W m −2 when normalized by anolyte contact area.
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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".