Interrelationship of Electric Double Layer Theory and Microfluidic Microbial Fuel Cells: A Review of Theoretical Foundations and Implications for Performance
Bibliographic record
Abstract
Microbial fuel cells and their related microfluidic systems have emerged as promising greener energy alternatives for the exploitation of avenues related to combined power and wastewater treatment operations. Moreover, the potential for their application in biosensing technology is large. However, while the fundamental principles of science that govern the design and operation of microbial fuel cells (MFCs) and microfluidic microbial fuel cells (MMFCs) are similar to those found in colloid science, the literature shows that current research lacks sufficient reference to the electrostatic and electrokinetic aspects, focusing mostly on aspects related to the architecture, design, anodes, microbial growth and metabolism, and electron transfer mechanisms. In this regard, research is yet to consider MFCs and MMFCs in the context of electrostatic and electrokinetic aspects. In this extensive review, we show, for the first time, the interrelationship of MFCs and MMFCs with electric double layer theory. Consequently, we show how the analytical solution to the mean field Poisson–Boltzmann theory relates to these systems. Moreover, we show the interrelationship between MFC and MMFCs’ performance and the electric double layer and the associated electrostatic and electrokinetic phenomena. This extensive review will likely motivate research in this direction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".