Electrospray Mass Spectrometry of Arsenic Compounds and Thiol–Arsenic Complexes
Bibliographic record
Abstract
Abstract This article reviews the identification and quantification of arsenic species and their complexes using electrospray ionization mass spectrometry (ESI‐MS). Topics covered include accurate mass determination, select ion monitoring (SIM) and multiple reaction monitoring (MRM), isotopic ratios, common fragmentation patterns, transition ratios, wrong‐way‐round ionization, crosstalk, ion interference, and matrix effects. The analytical methods range from direct‐infusion ESI‐MS and tandem mass spectrometry (MS/MS), to the coupling of the ESI‐MS to high‐performance liquid chromatography (HPLC) separation, and finally to combined analysis using HPLC separation with both inductively coupled plasma mass spectrometry (ICP‐MS) and HPLC/ESI‐MS detections. The combination of both ICP‐MS and ESI‐MS is especially powerful, benefiting from the established, robust, and sensitive element‐specific detection of ICP‐MS, and the molecular detection of ESI‐MS. Using the various mass spectrometry (MS) techniques, a variety of biological and environmental samples have been studied, including the urine of humans and animals, food and plants grown on arsenic‐contaminated soil, surface water, and contaminated groundwater. This article also discusses recent studies on the formation of thiol‐arsenic complexes between select arsenic species and thiols such as glutathione (GSH) and metallothionein (MT). The use of ESI‐MS contributes to determining the stoichiometry and binding location, monitoring the reaction in real time, and evaluating binding constants. Information from binding studies has helped the development of improved ESI‐MS methods through derivatization of arsenic species with thiols.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".