Optimized design and protocols eliminate power density gap between microbial fuel cells at different scales
Bibliographic record
Abstract
In addition to offering a promising approach for niche applications in environmental sensing and portable power sources, microfluidic microbial fuel cells (MFCs) can also accelerate the development of mainstream energy applications through studies into fundamental mechanisms and optimization, without complications from nutrient cycling, membrane fouling, or uncontrollable concentration gradients. However, the main hurdle in leveraging microfluidic MFCs for discovery and optimization is their underperformance compared to macrosystems on certain key metrics, notably area-normalized power. To bridge this gap, we showcase a strategy that focuses on (i) technology improvements, (ii) establishment of new performance benchmarks, and (iii) presentation of a universally applicable normalization method for direct comparisons across all MFC scales and that complements areal power densities. Using a pure-culture Geobacter sulfurreducens electroactive biofilm (EAB) applied to a new system that adheres to the strategy above, we observed optimal anode colonization, resulting in the highest recorded power density for a microfluidic MFC of 3.88 W m-2 (24.37 kW m-3) and a normalized energy recovery (0.21 kWh m-3) that nearly matches the average value observed in macrosystems. With these results, the performance gap between micro- and macroscale MFCs is closed, and a road map to move forward is presented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".