Polyaniline‐modified anodes for brewery waste treatment in microbial fuel cells: insights into inoculum selection, cell configuration, and lactic acid valorization
Bibliographic record
Abstract
Abstract BACKGROUND This study focuses on the treatment of brewery waste slurries (BWS) with high chemical oxygen demand (COD) using a single‐chamber microbial fuel cell (MFC) inoculated with heat‐treated anaerobic seed sludge. A co‐substrate of 5 g L−1 glycerol was added, and carbon felt (CF) was employed as the anode material, enhanced through in situ polymerization of aniline (PANI/CF). Additionally, the adsorption of ruthenium dioxide nanoparticles onto the modified CF was assessed (PANI‐RO/CF). RESULTS Tests conducted at room temperature (19 °C to 22 °C) achieved an average COD reduction of 36%. The PANI‐RO/CF electrode accumulated 24% more charge, resulting in the highest coulombic efficiency of 75.1%, significantly exceeding similar studies. However, the PANI‐modified anode generated more energy, exceeding that of the bare CF by more than double, reaching 57.1 mW m−2. An optimized working volume was identified in relation to other reported works. Microbial population analysis revealed an interaction between Staphylococcus epidermidis and a rarely reported psychrophilic Bacillus species. After 30 h, lactic acid emerged as the main by‐product, with a concentration of 7.5 ± 0.6 g L−1. CONCLUSIONS These findings highlight an optimization approach based on cell configuration and inoculum selection, as well as a significant valorization pathway that is frequently overlooked in the existing literature on brewery wastewater treatment using MFCs, particularly regarding the attractiveness of lactic acid production. © 2024 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".