A rapid and sensitive IC-ICP-MS method for determining selenium speciation in natural waters
Bibliographic record
Abstract
Selenium (Se) is an element monitored by water quality agencies worldwide. The challenge of assessing its presence in aquatic systems is its low concentrations (parts per trillion) and the need for determining its chemical speciation. A method was developed using an ion chromatograph (IC) paired with a quadrupole inductively coupled plasma mass spectrometer (ICP-MS) equipped with a hydrogen reaction cell to provide analysts with a rapid and sensitive method to measure Se speciation with suitable accuracy and precision. The Se species selenite (Se IV ) and selenate (Se VI ) were separated within a 5 min span using dilute nitric acid as a mobile phase in a step-wise gradient (50–400 mmol L −1 ) and quantified using 80 Se isotope that yielded low limits of detection (<10 ng L −1 ). Spectral interference from plasma generated diatomic argon ions ( 40 Ar 2 + ; m/z = 80) on 80 Se was eliminated by hydrogen gas (H 2 ) in the reaction cell. Polyatomic 79 Br 1 H + (m/z = 80) did not interfere with 80 Se for quantification of common aquatic Se species (Se VI and Se IV ) due to different column retention times. Two organic species (methylselenocysteine and selenomethionine) commonly found in aquatic and terrestrial plant tissues were also tested to rule out possible chromatographic interference and explore the potential application to biological samples. Urban rainwater and Canadian river water samples were analyzed for Se species to demonstrate the applicability of the method. Owing to its ability to rapidly determine Se species in water samples at environmentally relevant concentrations, the method may be useful for monitoring agencies to routinely measure Se species in freshwater aquatic systems.
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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 | 0.001 | 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".