Photocatalytic treatment of organoselenium in synthetic mine-impacted effluents
Bibliographic record
Abstract
Biological selenium reduction processes are commonly employed as the best available technology (BAT) for selenium removal; however, as a by-product they produce trace amounts of organoselenium compounds with orders of magnitude greater bioaccumulation potential and toxicity. Here, we assessed buoyant photocatalysts (BPCs) as a potential passive advanced oxidation process (P-AOP) for organoselenium treatment. Using a synthetic mine-impacted water solution, spiked with selenomethionine (96 μg/L) as a representative organoselenium compound, photocatalysis with BPCs fully eliminated selenomethionine to <0.01 μg/L with conversion to selenite and selenate. A theoretical reaction pathway was inferred, and a kinetics model developed to describe the treatment trends and intermediates. Given the known toxic responses of Lepomis macrochirus and Daphnia magna to organoselenium, it was estimated that photocatalysis could effectively eliminate organoselenium acute toxicity within a UV dose of 8 kJ/L (1-2 days solar equivalent exposure), by transformation of selenomethionine to less hazardous oxidized Se species. Solar photocatalysis may therefore be a promising passive treatment technology for selenium-impacted mine water management.
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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".