Advanced oxidation using Fe3O4 magnetic nanoparticles and its application in mercury speciation analysis by high performance liquid chromatography-cold vapor generation atomic fluorescence spectrometry
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Bibliographic record
Abstract
A novel, green and efficient post-column oxidation method using Fe(3)O(4) magnetic nanoparticles (MNPs) was developed to on-line convert hydride generation/cold vapor generation (HG/CV) inactive species to their active species without microwave/UV irradiation. It was applied to high performance liquid chromatography HG/CV atomic fluorescence spectrometry (HPLC-HG/CV-AFS) to enable sensitive speciation analysis of both HG/CV inactive and active species. Inorganic mercury (Hg(2+)), methylmercury (MeHg), ethylmercury (EtHg) and phenylmercury (PhHg) were selected as model compounds to validate the methodology. Separation of these mercury species was accomplished on a RP-C18 column with a mixture of acetonitrile and water (10 : 90) at pH 6.8 containing 0.12% (m/v) L-cysteine as the mobile phase. In the presence of 0.6% (v/v) H(2)O(2), on-line conversion of the organomercury species eluted from the HPLC column to Hg(2+) was obtained using the advanced oxidation method at pH 2.0. Optimum conditions for the separation, oxidation and cold vapor generation were carefully investigated. The limits of detection (LODs) were 0.7, 1.1, 0.8 and 0.9 μg L(-1) (as Hg) for Hg(2+), MeHg, EtHg and PhHg, respectively, corresponding to 14, 22, 16 and 18 pg absolute detection limits for Hg(2+), MeHg, EtHg and PhHg by using a 20 μL sample loop, which are comparable to or better than those previously reported. National Research Council Canada DORM-2 fish muscle tissue and several real water samples were analyzed to validate the accuracy of the proposed method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it