Lead(II) Removal at the Bioanode of Microbial Electrolysis Cells
Bibliographic record
Abstract
Abstract Advanced treatment, such as tight membrane filtration and ion exchange, can be applied for Pb 2+ removal from wastewater but these methods are expensive with a high demand for electric energy and chemicals. Microbial electrolysis cells (MECs) are an emerging wastewater treatment technology and MECs can remove Pb 2+ by reduction and precipitation at the cathode and biosorption at the anode; however, reduction at the anode has not been reported. We investigated Pb 2+ removal mechanisms using lab‐scale MECs. Using an anion exchange membrane, independent Pb 2+ removal in the anode and cathode chambers was observed at various voltage applications, including open circuit, 0.3 V, 0.6 V, and 0.9 V. A substantial amount of metallic Pb (0. 10 ± 0.02 mg) was found on the graphite fiber anode. Also, the observed anode potential (−0.15 to −0.33 V vs. SHE) indicated sufficient driving force for Pb 2+ reduction at the anode for the Pb 2+ concentration of 0.1 to 2.5 mg L −1 . Inactivation of exoelectrogens using ethanol resulted in no Pb 2+ removal. The findings show that Pb 2+ removal is achieved by various mechanisms in MECs, including electrodeposition at the anode by exoelectrogens.
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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 | 0.001 | 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".