Arsenic speciation analysis: A review with an emphasis on chromatographic separations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it