Removal of heavy metals in a flow‐through vertical microbial electrolysis cell
Bibliographic record
Abstract
This work describes laboratory experiments aimed at evaluating the feasibility of removing heavy metals from metal‐contaminated water in flow‐through microbial electrolysis cells (MECs) with peat moss as a source of organic carbon. MECs were assembled in upflow glass columns containing granular activated carbon (GAC) bioelectrodes preceded by a layer of peat moss. The MECs were fed with metal‐contaminated surface water collected at a firing range. At hydraulic retention times (HRT) of 4–6 days, up to 99 % removal of Pb, Zn, Cu, and Fe was observed. The removal efficiency of Zn and Cu declined at an HRT of 1.7 days, while effluent Pb concentration remained below the detection limit for all of the HRTs tested. Metal extraction from MEC compartments showed an accumulation of metals in both the peat moss layer and the GAC cathode, i.e., the removal was achieved by a combination of anaerobic and bioelectrochemical pathways of metal reduction. A proliferation of sulphate reducing bacteria in the peat moss layer and electroactive species in GAC electrodes was confirmed by biomolecular analysis. The proposed flow‐through system, which combines sulphate‐reducing and electroactive microbial activities to achieve near‐complete metal removal, can be used for removing a broad range of heavy metals from contaminated water in a low‐cost passive flow treatment system.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".