Removal of Arsenic from Synthetic Acid Mine Drainage by Electrochemical pH Adjustment and Coprecipitation with Iron Hydroxide
Bibliographic record
Abstract
Acid mine drainage (AMD), which is caused by the biological oxidation of sulfidic materials, frequently contains arsenic in the form of arsenite, As(III), and/or arsenate, As(V), along with much higher concentrations of dissolved iron. The present work is directed toward the removal of arsenic from synthetic AMD by raising the pH of the solution by electrochemical reduction of H+ to elemental hydrogen and coprecipitation of arsenic with iron(III) hydroxide, following aeration of the catholyte. Electrolysis was carried out at constant current using two-compartment cells separated with a cation exchange membrane. Four different AMD model systems were studied: Fe(III)/As(V), Fe(III)/As(III), Fe(II)/As(V), and Fe(II)/As(III) with the initial concentrations for Fe(III) 260 mg/L, Fe(II) 300 mg/L, As(V), and As(III) 8 mg/L. Essentially quantitative removal of arsenic and iron was achieved in all four systems, and the results were independent of whether the pH was adjusted electrochemically or by the addition of NaOH. Current efficiencies were approximately 85% when the pH of the effluent was 4-7. Residual concentrations of arsenic were close to the drinking water standard proposed by the World Health Organization (10 microg/L), far below the mine waste effluent standard (500 microg/L).
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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 | 0.000 | 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 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".