Selective hydride generation-cryotrapping-ICP-MS for arsenic speciation analysis at picogram levels: analysis of river and sea water reference materials and human bladder epithelial cells
Bibliographic record
Abstract
An ultra-sensitive method for arsenic (As) speciation analysis based on selective hydride generation (HG) with preconcentration by cryotrapping (CT) and inductively coupled plasma-mass spectrometry (ICP-MS) detection is presented. Determination of the valency of the As species is performed by selective HG without prereduction (trivalent species only) or with L-cysteine prereduction (sum of tri- and pentavalent species). Methylated species are resolved on the basis of thermal desorption of the formed methyl substituted arsines after collection at −196 °C. Limits of detection of 3.4, 0.06, 0.14 and 0.10 pg mL−1 (ppt) were achieved for inorganic As, mono-, di- and trimethylated species, respectively, from a 500 μL sample. Speciation analysis of river water (NRC SLRS-4 and SLRS-5) and sea water (NRC CASS-4, CASS-5 and NASS-5) reference materials certified to contain 0.4 to 1.3 ng mL−1 total As was performed. The concentrations of methylated As species in tens of pg mL−1 range obtained by HG-CT-ICP-MS systems in three laboratories were in excellent agreement and compared well with results of HG-CT-atomic absorption spectrometry and anion exchange liquid chromatography-ICP-MS; sums of detected species agreed well with the certified total As content. The HG-CT-ICP-MS method was successfully used for analysis of microsamples of exfoliated bladder epithelial cells isolated from human urine. Here, samples of lysates of 25 to 550 thousand cells contained typically tens of pg up to ng of iAs species and from single to hundreds of pg of methylated species, well within detection power of the presented method. A significant portion of As in the cells was found in the form of the highly toxic trivalent species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".