HPLC-ICP-MS for a comparative study on the extraction approaches for arsenic speciation in terrestrial plant, Ceratophyllum demersum
Bibliographic record
Abstract
For the determination of arsenic compounds in terrestrial plant samples, a crucial step is the efficient extraction of arsenic from the solid plant matrix. However, the use of methanol-water extraction often resulted in low extraction efficiencies of less than 50 %. In this study, eight solid-liquid extraction procedures (mainly based on mechanical mixing and sonication) were evaluated for the recovery of arsenic species from a submerged freshwater plant, coontail (Ceratophyllum demersum), collected in Moira River, Ontario, Canada. Speciation of As in the extracts was carried out with both anion-, and cation-exchange HPLC with sector-field inductively coupled plasma mass spectrometric (SF-ICP-MS) detection. The results obtained depended critically on the extraction solvents used in different extraction procedures. Extraction with methanol-water led only to 9 %  44 % recoveries of As. A high extraction yield (approximately 82 %) was obtained by water extraction. Alkaline hydrolysis also resulted in high extraction efficiencies (86 %  98%), but severe oxidation of As(III) to As(V) was observed. A protease enzymatic extraction led to a recovery of 48 %. Approximately 0.5 % of the total As in the plant sample was lipid-soluble. It was found that the extraction of inorganic arsenic species was suppressed by the presence of methanol in the extraction solvents, while high content of methanol in the extraction solvents was effective for the extraction of organic arsenic species. Therefore, it is recommended to perform the extraction both with water alone and with methanol-water (9+1, v/v), in order to obtain the complete As species profile in terrestrial plants.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".