Microwave Digestion of Environmental and Natural Waters for Selenium Speciation
Bibliographic record
Abstract
A microwave preparation procedure is proposed for selenium speciation in natural and drinking waters. Different chemical reagents were tested, and the conditions for Se speciation were optimized. The effect of the different reagents on various oxidation states of selenium under microwave digestion conditions was investigated. Most of the Se(-II) was converted to selenite when digested with HNO3 and <20% to selenate. The digestion with H2O2/H2SO4 can change most Se species into Se(IV). The concentration of Se(IV) in the samples was then determined by HPLC with a fluorescence detector after derivatization with 2,3-diamino-naphthalene (DAN). The microwave preparation procedure allows Se speciation in water samples. Se(IV) was determined after concentrating the sample under nitrogen protection. The amount of Se(IV) and Se(VI) was measured by adding an equal volume of concentrated hydrochloric acid to water sample to reduce Se(VI) to Se(IV). Then the amount of Se(VI) can be calculated by subtraction. The total selenium can be determined after digestion with H2O2/H2SO4, or after digestion with HNO3 followed by reduction with concentrated hydrochloric acid. Selenium (-II, 0) was calculated by subtracting inorganic Se(IV+VI) from the total. Detection limits of 0.0066 ng and 0.0096 ng Se were obtained for HNO3 and H202/H2SO4 as digestion reagents, respectively. The total Se in the four water samples tested range from 0.20 to 0.90 microg L(-1). Among them the dominant form was Se(VI) with the exception of pond waters where Se(-II) predominated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".