Harvesting Energy from Multiple Microbial Fuel Cells with a High-Conversion Efficiency Power Management System
Bibliographic record
Abstract
Direct electricity production from waste biomass in a microbial fuel cell (MFC) offers the advantage of producing renewable electricity at a high Coulombic efficiency. However, low MFC voltage (below 0.5 V) necessitates the simultaneous operation of multiple MFCs controlled by a power management system (PMS) adapted for operating bioelectrochemical systems with complex nonlinear dynamics. This work describes a novel PMS designed for efficient energy harvesting from multiple MFCs. The PMS includes a switched-capacitor-based converter, which ensures operation of each MFC at its maximum power point (MPP) by regulating the output voltage around half of its open-circuit voltage. The open-circuit voltage of each MFC is estimated online regardless of MFC internal parameter knowledge. The switched-capacitor-based converter is followed by an upconverter, which increases the output voltage to a required level. Advantages of the proposed PMS include online MPP tracking for each MFC and high (up to 85%) power conversion efficiency. Also, the PMS prevents voltage reversal by disconnecting an MFC from the circuit whenever its voltage drops below a predefined threshold. The effectiveness of the proposed PMS is verified through simulations and experimental runs.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".