Identification and Determination of Selenosulfate and Selenocyanate in Flue Gas Desulfurization Waters
Bibliographic record
Abstract
In this work, 13 selenium species in flue gas desulfurization (FGD) waters from coal-fired power plants were separated and quantified using anion-exchange chromatography coupled to inductively coupled plasma mass spectrometry. For the first time, we identified both selenosulfate (SeSO(3)(2-)) and selenocyanate (SeCN(-)) in such waters, using retention time matching and confirmation by electrospray mass spectrometry. Besides selenite and selenate, selenosulfate was the most frequently occurring selenium species. It occurred in most samples and constituted a major fraction (up to 63%) of the total selenium concentration in waters obtained from plants employing inhibited oxidation scrubbers. Selenocyanate occurred in about half of the tested samples, but was only a minor species (up to 6% of the total selenium concentration). Nine additional Se-containing compounds were found in FGD waters, but they remain unidentified at this point.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".