Arsenic speciation analysis: A review with an emphasis on chromatographic separations
Bibliographic record
Abstract
More than 100 different arsenic species of diverse characteristics are present in the environment and biological systems. The identification and quantification of individual arsenic species are critical to understanding the distribution, environmental fate and behaviour, metabolism, and toxicity of arsenic. This review summarizes sample preparation, separation, detection, and method validation for arsenic speciation analysis. An emphasis is placed on chromatographic separation techniques, relating the physicochemical properties of arsenic species to their efficient separation. Anion exchange, cation exchange, reversed-phase, ion pair, and size exclusion chromatography are useful to separate various arsenic species. Recent research has explored hydrophilic interaction liquid chromatography (HILIC), multiple separation mechanisms, and testing of fluorophenyl and graphene oxide stationary phases for the separation of arsenic species. Sample preparation, extraction of arsenic species, recovery of arsenic species from separation columns, and method validation are discussed in light of their importance to the integrity and accuracy of speciation analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".