Hyphenation of high performance liquid chromatography with sector field inductively coupled plasma mass spectrometry for the determination of ultra-trace level anionic and cationic arsenic compounds in freshwater fishElectronic Supplementary Information (ESI) available: analytical figures of merit obtained with a standard set-up, the LOD for the high resolution mode, LODs reported by other authors and tissue concentrations for the Northern pike. See http://www.rsc.org/suppdata/ja/b3/b304890j/
Bibliographic record
Abstract
We describe a hyphenation technique between HPLC and ICP-SFMS for ultra-trace arsenic speciation analysis. Exceptional analytical performance was achieved using a MicroMist nebulizer preceded by a high-pressure splitter. Despite a 1 ∶ 7.5 flow splitting, the detection limits in the range of 1.2 to 2.4 pg mL−1 were about two times lower than those obtained with a concentric nebulizer without any flow splitting, demonstrating the applicability of coupling conventional HPLC system (1.5 mL min−1 eluent flow) with microflow nebulizer for ultra-trace arsenic speciation analysis. This set-up offers an advantage for on-line fraction collection for either multidimensional chromatographic separation of co-eluting As compounds or for structural identification of unknown compounds without sacrificing analytical sensitivity. In addition, this system showed good accuracy and repeatability. The method was applied to the determination of arsenic compounds in freshwater fish samples from an arsenic-rich lake, Moira Lake, Canada. Using cation-exchange chromatography, tetramethylarsonium ion (Tetra) was detected in freshwater fish samples for the first time. Moreover, in pumpkinseed, Tetra was found to be the major arsenic species, indicating that the biomethylation pathway in freshwater ecosystems may include the tetramethyl stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".