A biomembrane grown in situ for improved microfluidic microbial fuel cell performance using a pure culture Geobacter sulfurreducens electroactive biofilm
Bibliographic record
Abstract
Microfluidic microbial fuel cells (MFCs) hold great potential to reproduce core functions of bulk MFCs for study and optimization under precise conditions. Unlike most MFC types, those in a microfluidic format typically do not use a membrane to separate anode and cathode compartments, relying instead on the physics of laminar flow to maintain isolation of independent liquid streams. This lowers cost, device complexity, and should reduce internal resistance. However, to avoid solution crossover, which is likely to occur due to inevitable instabilities during long operational times, authors often separate electrodes by distances of several millimeters or more. This reverses benefits on internal resistance, undermining a prime advantage of microfluidic MFCs. This work demonstrates a facile method for the in-situ synthesis of a microscale membrane, supporting sub-milimeter electrode spacing. The membrane added only 68.5 Ω to the cell internal resistance and its synthesis resulted in no measurable changes to Rct at either electrode. However, the method to grow the membrane after device synthesis greatly reduced complexity in device fabrication. Overall, the reduced electrode spacing that was facilitated by the membrane lowered internal resistance from 25 k to 10 k and provide stable operation even under non-ideal flow conditions. Compared to a state-of-the-art membraneless MFC with 6 mm electrode spacing, the membrane MFC provided approximately 45% higher power density, 290% higher current density and 7 times higher acetate conversation efficiency. Membrane-enhanced flow stability also delivered continuous increases to power density with increased flow rate over baseline levels, rising to 30% higher for flow rate increases of 100 times.
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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 | 0.000 |
| 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 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".