Ionic Liquid Ferrofluid-Based Preconcentration And Ultra-Trace Determination Of As And Se Species In Complex Matrices Using Inductively Coupled Plasma Mass Spectrometry
Bibliographic record
Abstract
This paper describes the use of an ionic liquid ferrofluid for the preconcentration and simultaneous ultra-trace determination of inorganic As and Se species in waters by inductively coupled plasma mass spectrometry.An ultrasound-assisted sol-gel method was used for the synthesis of silica and titania coated and N-(2-aminoethyl)-3-aminopropyltrimethoxysilane functionalized magnetic nanoparticles (SCTCMNPs-AEAPTMS).The structural features of the SCTCMNPs-AEAPTMS were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy with energy dispersive X-ray spectroscopy, X-ray diffraction, and transmission electron microscopy.Experimental conditions, including the sample solution pH, elution time, and eluent concentration, were optimized.After oxidation of As(III) and Se(IV) to As(V) and Se(VI) by using H2O2, the total concentrations of As and Se were determined and those of As(III) and Se(IV) were obtained through subtraction of the concentration of As(V) and Se(VI) from the total concentrations.Under the optimal experimental conditions, the detection limit for As(V) and Se(VI) were 0.3 ng L -1 and 0.2 ng L -1 respectively.The accuracy of this method was verified by analyzing a certified reference material (1568a Rice Flour): the measured As and Se concentrations agreed with the certified values based on a Student's t-test at the 95% confidence level.The proposed method was also successfully applied to the preconcentration and ultra-trace determination of As and Se species in different water samples.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 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".