Selective Gas Chromatography Mass Spectrometry Method for Ultratrace Detection of Selenocyanate
Bibliographic record
Abstract
The recent interest in the determination of selenocyanate (SeCN – ) in wastewater systems has spurred the development of analytical methods for its determination at the ultratrace level. Since most of the current procedures require complex and costly instrumental configurations, we have developed a simple and rapid gas chromatography tandem mass spectrometry (GC/MS/MS) method able to detect SeCN – in water samples with a LOD of 0.1 ng/g Se. A 1 mL volume of aqueous sample was buffered with sodium bicarbonate and treated with triethyloxonium tetrafluoroborate for conversion of the analyte into volatile EtSeCN. The derivatization yield was higher than 90%, and it could tolerate concentrations of chloride or sulfate up to 2%. The EtSeCN was extracted in chloroform and could be detected in electron ionization and also in negative chemical ionization mode with a further gain in signal-to-noise ratio by a factor of 2. The method was applied for the analysis of natural waters with quantitation of SeCN – in the low ng/g region. The Se 13 C 15 N – internal standard could be used for isotope dilution. Quantitative spike recoveries of 1 ng/g Se were obtained from seawater and river water, and 1 ng/g Se could be quantified within a standard uncertainty of 15%.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".