A Systematic Study of Separators in Air-Breathing Flat-Plate Microbial Fuel Cells—Part 1: Structure, Properties, and Performance Correlations
Bibliographic record
Abstract
Passive air-breathing microbial fuel cells (MFCs) are a promising technology for energy recovery from wastewater and their performance is highly dependent on characteristics of the separator that isolates the anaerobic anode from the air-breathing cathode. The goal of the present work is to systematically study the separator characteristics and its effect on the performance of passive air-breathing flat-plate MFCs (FPMFCs). This was performed through characterization of structure, properties, and performance correlations of eight separators in Part 1 of this work. Eight commercial separators were characterized, in non-inoculated and inoculated setups, and were examined in passive air-breathing FPMFCs with different electrode spacing. The results showed a decrease in the peak power density as the oxygen and ethanol mass transfer coefficients in the separators increased, due to the increase of mixed potentials especially at smaller electrode spacing. Increasing the electrode spacing was therefore desirable for the application of diaphragms. The highest peak power density was measured using Nafion®117 with minimal electrode spacing, whereas using Nafion®117 or Celgard® with larger electrode spacing resulted in similar peak powers. Part 2 of this work focuses on numerical modelling of the FPMFCs based on mixed potential theory, implementing the experimental data from Part 1.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".