Rapid and Efficient Arsenic Removal by Iron Electrocoagulation Enabled with in Situ Generation of Hydrogen Peroxide
Bibliographic record
Abstract
Millions of people are exposed to toxic levels of dissolved arsenic in groundwater used for drinking. Iron electrocoagulation (FeEC) has been demonstrated as an effective technology to remove arsenic at an affordable price. However, FeEC requires long operating times (∼hours) to remove dissolved arsenic due to inherent kinetics limitations. Air cathode Assisted Iron Electrocoagulation (ACAIE) overcomes this limitation by cathodically generating H2O2 in situ. In ACAIE operation, rapid oxidation of Fe(II) and complete oxidation and removal of As(III) are achieved. We compare FeEC and ACAIE for removing As(III) from an initial concentration of 1464 μg/L, aiming for a final concentration of less than 4 μg/L. We demonstrate that at short electrolysis times (0.5 min), i.e., high charge dosage rates (1200 C/L/min), ACAIE consistently outperformed FeEC in bringing arsenic levels to less than WHO-MCL of 10 μg/L. Using XRD and XAS data, we conclusively show that poor arsenic removal in FeEC arises from incomplete As(III) oxidation, ineffective Fe(II) oxidation and the formation of Fe(II–III) (hydr)oxides at short electrolysis times (<20 min). Finally, we report successful ACAIE performance (retention time 19 s) in removing dissolved arsenic from contaminated groundwater in rural California.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".