Evaluation of Mixed Valent Iron Oxides as Reactive Adsorbents for Arsenic Removal
Bibliographic record
Abstract
The objective of this research was to determine if Fe(II)-bearing iron oxides generate ferric hydroxides at sufficient rates for removing low levels of arsenic in packed-bed reactors, while at the same time avoiding excessive oxide production that contributes to bed clogging in oxygenated waters. Column experiments were performed to determine the effectiveness of three media for arsenic removal over a range in empty bed contact times, influent arsenic concentrations, dissolved oxygen (DO) levels, and solution pH values. Corrosion rates of the media as a function of the water composition were determined using batch and electrochemical methods. Rates of arsenic removal were first order in the As(V) concentration and were greater for media with higher corrosion rates. As(V) removal increased with increasing DO levels primarily due to faster oxidation of the Fe2+ released by media corrosion. To obtain measurable amounts of arsenic removal in 15 mM NaCl electrolyte solutions containing 50 microg/L As(V), the rate of Fe2+ released by the media needed to be at least 15 times greater than the As(V) feed rate into the column. In waters containing 30 mg/L of silica and 50 microg/L of As(V), measurable amounts of arsenic removal were obtained only for Fe2+ release rates that were at least 200 times greater than the As(V) feed rate. Although all columns showed losses in hydraulic conductivity overthe course of 90 days of operation, the conductivity values remained high, and the losses could be reversed by backwashing the media. The reaction products produced by the media in domestic tap water had average As-to-Fe ratios that were approximately 25% higher than those for a commercially available adsorbent.
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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".