Real-Time Performance Optimization and Diagnostics during Long-Term Operation of a Solid Anolyte Microbial Fuel Cell Biobattery
Bibliographic record
Abstract
This study describes a novel approach for real-time energy harvesting and performance diagnostics of a solid anolyte microbial fuel cell (SA-MFC) representing a prototype smart biobattery. The biobattery power output was maximized in real time by combining intermittent power generation with a Perturbation-and-Observation algorithm for maximum power point tracking. The proposed approach was validated by operating the biobattery under a broad range of environmental conditions affecting power production, such as temperature (4–25 °C), NaCl concentration (up to 2 g L−1), and carbon source concentration. Real-time biobattery performance diagnostics was achieved by estimating key internal parameters (resistance, capacitance, open circuit voltage) using an equivalent electrical circuit model. The real time optimization approach ensured maximum power production during 388 days of biobattery operation under varying environmental conditions, thus confirming the feasibility of biobattery application for powering small electronic devices in field applications.
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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 | 0.000 | 0.001 |
| 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 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".