In-situ electrochemical Fe(VI) for removal of microcystin-LR from drinking water: comparing dosing of the ferrate ion by electrochemical and chemical means
Bibliographic record
Abstract
Abstract Harmful algal blooms (HAB) release microtoxins that contaminate drinking water supplies and risk the health of millions annually. Crystalline ferrate(VI) is a powerful oxidant capable of removing algal microtoxins. We investigate in-situ electrochemically produced ferrate from common carbon steel as an on-demand alternative to crystalline ferrate for the removal of microcystin-LR (MC-LR) and compare the removal efficacy for both electrochemical (EC) and chemical dosing methodologies. We report that a very low dose of EC-ferrate in deionized water (0.5 mg FeO42− L−1) oxidizes MC-LR (MC-LR0 = 10 μg L−1) to below the guideline limit (1.0 μg L−1) within 10 minutes' contact time. With bicarbonate or natural organic matter (NOM), doses of 2.0–5.0 mg FeO42− L−1 are required, with lower efficacy of EC-ferrate than crystalline ferrate due to loss of EC-ferrate by water oxidation. To evaluate the EC-ferrate process to concurrently oxidize micropollutants, coagulate NOM, and disinfect drinking water, we spiked NOM-containing real water with MC-LR and Escherichia coli, finding that EC-ferrate is effective at 10.0 mg FeO42− L−1 under normal operation or 2.0 mg FeO42− L−1 if the test water has initial pH optimized. We suggest in-situ EC-ferrate may be appropriate for sporadic HAB events in small water systems as a primary or back-up technology.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".