Effect of nanowire conductive transfer on the performance of batch‐microbial fuel cells
Bibliographic record
Abstract
Microbial fuel cells (MFCs) are a promising technology that uses microorganisms to simultaneously generate bioelectricity while treating wastewater. To further improve the performance of the MFC, it is essential to understand and evaluate the electron transfer mechanism. However, redesigning the electron transfer mechanism of MFCs through an experimental approach is costly and time-consuming. Hence, in this study, a numerical modeling approach is implemented through the Nernst-Monod kinetic equation, which is validated by experimental results. A nanowire conductive transferring pathway is considered between the microorganisms and anode electrodes of a batch-type MFC. Moreover, two types of bacteria are utilized such as the Shewanella oneidensis MR-1 and Shewanella putrefacient with substrate concentrations of 0.5 M sodium lactate. The results have shown that the limiting current density of the MFC from the computational model is 1514 mA m−2. On the other hand, the current density from the experimental approach for Shewanella oneidensis MR-1 is 497 mA m−2 while for Shewanella putrefacient is 140 mA m−2. The anode activation loss of 491 Ω is lower than the cathode activation loss of 643 Ω, which indicates the relative influence of the cathode activation loss on the bioelectricity generation of the MFC. In addition, the results revealed that the nanowire electron transfer mechanism in the anode biofilm was less affected by the concentration losses. This then indicates that the physical mechanism of the nanowire electron transfer can be effectively used to investigate the batch-type MFCs. In turn, the results of this study will contribute to the development of an improved MFC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".