Determination of five arsenic species in aqueous samples by HPLC coupled with a hexapole collision cell ICP-MS
Bibliographic record
Abstract
A new method has been developed for the simultaneous determination of AsV, monomethylarsenic (MMA), dimethylarsenic (DMA), AsIII, and arsenobetaine (AsB) in aqueous samples. The method utilizes a multi-mode ion exchange column coupled with a hexapole collision cell inductively coupled plasma mass spectrometer (ICP-MS). A mixture of 10 mM NH4NO3 and 0.05% HNO3 was used as the mobile phase, pumped by a high-performance liquid chromatography (HPLC) pump running in an isocratic mode at a flow rate of 0.4 mL min−1. Under these conditions, the five As species were separated within 14 min. The column outlet was connected directly to the ICP-MS via a low flow Meinhard concentric nebulizer. A river water certified reference material (SLRS-4), spiked with five As species at 20 µg L−1 (As cation) each, was used to validate the chromatographic separation and quantification in a real sample matrix. Based on replicate analyses of SLRS-4, the precision varied from 2% for AsV to 10% for MMA. The detection limits (3s) ranged from 0.02 µg L−1 for AsB to 0.4 µg L−1 for MMA. The new method was applied to water samples collected during As toxicity test experiments. The results indicated that transformation of inorganic As species (AsV and AsIII) occurred during the toxicity test experiments, but no methylation of inorganic As species was detected.
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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 | 0.001 |
| 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) | 0.001 | 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".