A Novel Algal–Algal Microbial Fuel Cell for Enhanced Chemical Oxygen Demand Removal
Bibliographic record
Abstract
To enhance the removal of COD (Chemical Oxygen Demand) by microalgae, this study constructed a novel microalgae–microalgae microbial fuel cell system (AA-MFC). It investigated the coupling relationship between the COD treatment efficiency at the anode and the production of high-value microalgal products at the cathode, as well as explored the effects of different initial inoculum densities and light–dark cycles. The experiment first measured the operational performance of the newly constructed AA-MFC in open-circuit and closed-circuit modes, demonstrating that this novel AA-MFC could start up rapidly within 32 h and operate stably. The results showed that the AA-MFC enhanced the removal of COD and the growth of microalgae biomass at the anode while maintaining stable power generation. When the initial inoculation density of the anode was 1.2 × 108 cell/cm2 and the light–dark cycle time was 18:6 h, the AA-MFC had the most obvious promoting effect on the COD removal of the anode. Compared with normal culture conditions, the COD removal rate increased by 26.0% to 96.1%. These results indicate that the AA-MFC can not only effectively remove pollutants, but also promote the accumulation of high-value microalgae biomass.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".