Single-Drop Solution Electrode Discharge-Induced Cold Vapor Generation Coupling to Matrix Solid-Phase Dispersion: A Robust Approach for Sensitive Quantification of Total Mercury Distribution in Fish
Bibliographic record
Abstract
Sensitive quantification of mercury distribution in fish is challenging because of insufficient sensitivities of conventional analytical methods, the limited mass of organs (tens of micrograms to several milligrams), and dilution of analyte concentration from sample digestion. In this work, a simple and robust approach coupling multiwall carbon nanotubes assisted matrix solid-phase dispersion (MWCNTs-MSPD) to single-drop solution electrode glow discharge-induced cold vapor generation (SD-SEGD-CVG) was developed for the sensitive determination of mercury in limited amount of sample. Mercury species contained in a limited amount of sample can be efficiently extracted into a 100 μL of eluent by MWCNTs-MSPD, which are conveniently converted to Hg 0 by SD-SEGD-CVG and further transported to atomic fluorescence spectrometry for their determination. Therefore, analyte dilution resulted from sample preparation is avoided and sensitivity is significantly improved. On the basis of consumption of 1 mg of sample, a limit of detection of 0.01 μg L –1 (0.2 pg) was obtained with relative standard deviations (RSDs) of 5.2% and 4.6% for 2 and 20 μg L –1, respectively. The accuracy of the proposed method was validated by analysis of three Certified Reference Materials with satisfying results. To confirm that SD-SEGD-CVG-AFS coupling to MWCNTs-MSPD is a promising method to quantify mercury distribution in fish, this method was successfully applied for the sensitive determination of mercury in seven organs of common carps (muscle, gill, intestine, liver, gallbladder, brain, and eye) after dietary of mercury species. The proposed method provides advantages of minimum sample dilution, low blank, high sample introduction efficiency, high sensitivity, and minimum toxic chemicals and sample consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".