Optimization of Bioenergy Generation via Coupled Thermo-Hydro-Electrochemical Modeling of Two-Chamber Microbial Fuel Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microbial fuel cells (MFCs) have drawn increasing attention as a sustainable approach for simultaneous wastewater remediation and energy production. However, their efficiency remains constrained by challenges in cathodic reaction kinetics, electrode performance, and system control. In this study, a three-dimensional multiphysics model of a dual-chamber MFC was developed using COMSOL, coupling electrochemical reactions, fluid dynamics, and ion transport. Experimental data validated the model, with a maximum deviation of 3.15% at low to moderate current densities. The simulation highlights that electric potential gradients and ionic currents are predominantly distributed near the electrode surfaces, while central regions exhibit higher current density. Moderate acetate flow rates (0.015–0.020 m/s) enhance substrate delivery but may destabilize the biofilm if increased excessively. Elevated feed concentrations (3.5–5.0 mol/m3) improve power density, although oversupply risks microbial imbalance and substrate accumulation. Temperature variation between 303 and 323 K has a limited effect on power density but supports enzymatic reactions and electron transfer efficiency. A sensitivity assessment ranks substrate concentration and flow rate as the most influential parameters, while temperature plays a secondary role. Based on these insights, a hierarchical optimization strategy is proposed—first optimizing substrate availability and residence time, then improving hydrodynamic conditions, and finally regulating temperature within a suitable range. This work provides theoretical support for advancing MFC design and operation in sustainable water-energy systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it