A Critical Investigation of Hydride Generation-Based Arsenic Speciation in Sulfidic Waters
Bibliographic record
Abstract
In sulfidic environments, hydride generation-based approaches are not suitable for arsenic determination because thioarsenates which can constitute the predominant arsenic species under these conditions (> 80% of total arsenic) are completely ignored. Sample acidification for preservation or during hydride generation leads to loss of total inorganic arsenic due to precipitation of arsenic-sulfur phases. Total concentrations can be determined correctly using 1% potassium iodide as prereducing agent while with L-cysteine (0.16 mol L(-1)), transformation of tetra-, tri-, and dithioarsenate to arsenite remains incomplete. By decreasing the original sample pH, hydride generation destroys thioarsenate species distribution because only monothioarsenate is stable over the whole pH range. Dithioarsenate transforms to arsenite below pH 4. Tetrathioarsenate transforms to trithioarsenate (pH 11.9) which subsequently transforms to arsenite (pH 5.6), followed by precipitation of arsenic-sulfur phases below pH 5. It is thus impossible to determine thioarsenates by hydride generation. The "As(III)"--fraction contains tetra-, tri-, and some dithioarsenate as well as arsenite, while monothioarsenate is determined with arsenate as "As(V)". Different analytical setups have substantial impact on thioarsenate hydride-generation behavior, thus provide little comparability and render reinterpretation of existing arsenic hydride-generation speciation data from sulfidic environments impossible. In natural geothermal water samples from Yellowstone National Park, total arsenic concentrations determined by ICP-MS and by HG-AFS with prereductant agreed well (< 6% relative difference). Speciation results deviated from the behavior predicted for thioarsenates from laboratory experiments, probably due to matrix effects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".