Geometric effect of honeycomb on the performance of the recirculation microbial fuel cells
Bibliographic record
Abstract
Summary Microbial fuel cells (MFCs) are recognized as a state‐of‐the‐art technology that generates biochemical energy and converts it to electrical energy. MFCs include a series of metabolizing organic material from wastewater and allow its treatment while providing the opportunity to generate electricity. It is to be noted that the buffer used commonly in MFCs is relatively costly and quantified to have environmental impacts when applied in commercial wastewater treatment. To address the concern related to the buffer, this work proposes to evaluate a geometrical design of honeycomb whose inner diameters (0.4, 0.7, 1.2 cm) and lengths (2.5, 5 cm) were selected to replace the buffer. With the introduction of the honeycomb design, the study also aims to investigate its effect on the performance of recirculation within the MFCs. This is then evaluated under the optimal operational flow rate and pH level, which were already established by previous studies. The results have revealed that the optimal geometry of the honeycomb consists of a dimension with an inner diameter of 1.2 cm and a length of 5 cm. This combination of inner diameter and length of the honeycomb has yielded the highest power density for the MFC at 491 mW m−2 when compared with the other cases. The findings of this study will be useful for the development of a cost‐effective and environmentally friendly MFC when applied in commercial wastewater treatment in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".