Dissolved Selenium(VI) Removal by Zero-Valent Iron under Oxic Conditions: Influence of Sulfate and Nitrate
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Dissolved Se(VI) removal by three commercially available zero-valent irons (ZVIs) was examined in oxic batch experiments under circumneutral pH conditions in the presence and absence of NO 3 – and SO 4 2– . Environmentally relevant Se(VI) (1 mg L –1 ), NO 3 – ([NO 3 —N] = 15 mg L –1 ), and SO 4 2– (1800 mg L –1 ) were employed to simulate mining-impacted waters. Ninety percent of Se(VI) removal was achieved within 4–8 h in the absence of SO 4 2– and NO 3 – . A similar Se(VI) removal rate was observed after 10–32 h in the presence of NO 3 – . Dissolved Se(VI) removal rates exhibited the highest decrease in the presence of SO 4 2–; 90% of Se(VI) removal was measured after 50–191 h for SO 4 2– and after 150–194 h for SO 4 2– plus NO 3 – depending on the ZVI tested. Despite differences in removal rates among batches and ZVI materials, Se(VI) removal consistently followed first-order reaction kinetics. Scanning electron microscopy, Raman spectroscopy, and X-ray diffraction analyses of reacted solids showed that Fe(0) present in ZVI undergoes oxidation to magnetite [Fe 3 O 4 ], wüstite [FeO], lepidocrocite [γ-FeOOH], and goethite [α-FeOOH] over time. X-ray absorption near-edge structure spectroscopy indicated that Se(VI) was reduced to Se(IV) and Se(0) during removal. These results demonstrate that ZVI can be effectively used to control Se(VI) concentrations in mining-impacted waters.
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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) | 0.001 | 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".