Phototrophic microbial fuel cells for sustainable power generation and wastewater treatment
Bibliographic record
Abstract
Microbial fuel cells (MFCs) rely on the capacity of electrode-adhered electroactive bacteria to oxidize organic matter and generate electrons. Typical MFCs are highly engineered systems that can be applied as green tools to alleviate the burden of waste streams. Phototrophic MFCs (PhMFCs) are a promising variant that can be implemented indoors or outdoors and use the power of the sun to boost efforts in on-site environmental remediation, biomass generation, and power generation. PhMFC variations include plant-based and algal-based MFCs. Algal-based MFCs can incorporate special photosynthetic action at either the anode or cathode, enhancing or replacing the role of other bacteria in regular bacterial MFCs. Plant-based MFCs can be more complex due to the role of the root system near an electrode and its interaction with electrode-adhered bacteria, and they are nearly universally operated outdoors in either natural or engineered conditions. This review considers PhMFCs such as algal-based MFCs, algal carbon capture cells (ACCCs) and anode algal microbial fuel cells (AAMFCs), and also plant-based MFCs which include natural plant MFCs (NPMFCs), constructed wetland MFCs (CWMFCs), and marine-rooted plant MFCs (MPMFCs). After summaries of the fabrication and function of different PhMFCs, we elaborate with a literature review and discussion on each variant, followed by suggestions for future directions that will enhance the impact and accelerate the uptake of these promising multi-functional biosystems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".