Using the Multimode Sample Introduction System (MSIS) for Low Level Analysis of Arsenic and Selenium in Water
Bibliographic record
Abstract
In recent years, the problems associated with the measurement of low concentrations of arsenic (As) and selenium (Se) using conventional nebulization and inductively coupled plasma–atomic emission spectroscopy (ICP–AES) have been largely overcome by using hydride generation. In 2002, a radically new design for the combined nebulizer/gas liquid separator referred to as the Multimode Sample Introduction System (MSIS, Marathon Scientific, Niagara Falls, Ontario, Canada) was introduced. The feasibility and detection limits of combining the MSIS with a Spectro Ciros CCD (axial) ICP–AES for the determination of low concentrations of As and Se in water were examined. Overall, the system was inexpensive, easy to install, accurate and precise, and lowered the quantification limits by approximately 100‐fold for As and by 20‐fold for Se as compared with a conventional nebulization. Unlike other conventional hydride generators, the MSIS does not need to be removed from the ICP when analyzing other elements without hydride generation. Therefore, the MSIS is recommended to laboratories seeking low detection limits for As and Se with existing or new ICP instruments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".