Speciation of Submicrogram per Liter Levels of Arsenic in Water: On-Site Species Separation Integrated with Sample Collection
Bibliographic record
Abstract
Speciation of arsenic is crucial for assessing health implications from arsenic ingestion and for effective removal of arsenic from water. We report a method for the speciation of submicrogram per liter levels of arsenic in water. The method incorporates water sample collection with on-site arsenic species separation. The method is based on selective retention of arsenic species on specific solid-phase cartridges followed by selective elution and hydride generation atomic fluorescence analysis of the arsenic species. The use of a membrane filter, a resin-based strong cation-exchange cartridge, and a silica-based strong anion-exchange cartridge allows for the speciation of particulate arsenic and soluble arsenite, arsenate, monomethylarsonate, and dimethylarsinate species. Detection limit is on the order of 0.05 μg/L. The method is suitable for direct water sample collection and on-site separation of arsenic species by flowing a measured volume of water sample through the filter and cartridges connected in serial. A particular advantage of this approach is to maintain the integrity of original arsenic species in the sample. It overcomes the common problem of instability of arsenic species after water sampling and during sample storage and handling. Applications of the method are demonstrated to the speciation of arsenic in well water, raw (untreated) river water, bottled water, and a standard reference material (SRM 1643d). Results agree well with the certified values of the SRM.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".