Selenium in wastewater: fast analysis method development and advanced oxidation treatment applications
Bibliographic record
Abstract
Selenium, a ubiquitous non-metal in nature, is potentially toxic to natural ecosystems due to its bioaccumulation potential. Due to increased monitoring and enforcement of selenium regulations, the need to be able to measure and treat selenium efficiently has taken on an increased importance. The principal aqueous forms of inorganic selenium are selenite (Se(IV)) and selenate (Se(VI)). Selenate, due to its high mobility and lack of affinity to conventional adsorbents, is typically much more difficult to treat and remove. To address both measurement and removal, an analytical method is reported for quantification of selenium in wastewater (WW) using UV-Vis spectrophotometer followed by removal studies using advanced oxidation processes (AOPs). Malachite green and azure blue were selected for colorimetric analysis using UV-Vis. Malachite green indicator showed the best results for analysis. The reported UV-Vis method was applied to establish the effect of AOPs on selenium removal. It was noted that all of the AOP treated samples showed removal of selenium and it was established that the UV-Vis method has a lower limit of detection at 2 mg/L. Further, through this study, it was found that the chemical cavitation yield and selenium removal efficiency peaked at low frequency ultrasound of 40 kHz.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".