Microwave treatment of biological samples for methylmercury determination by high performance liquid chromatography–cold vapour atomic fluorescence spectrometry
Bibliographic record
Abstract
A simple and rapid microwave-assisted alkaline digestion procedure was developed in combination with high performance liquid chromatography-ultraviolet post-column oxidation-cold vapour atomic fluorescence spectrometric detection for methylmercury determination in biological tissues. Since the stability of methylmercury in methanolic potassium hydroxide solution under microwave irradiation was verified, the microwave-assisted extraction procedure was optimized in terms of quantitative recovery of methylmercury and minimum time required. The alkaline extracts were subjected to clean-up steps with dichloromethane and hydrochloric acid in order to reduce matrix interferences in methylmercury determination. The effects of matrix interferences were checked by comparison of the slopes corresponding to calibration and standard addition curves. The accuracy of the method was evaluated by the analysis of two biological certified reference materials, NRC TORT-2 and BCR 463. The results obtained by the proposed method were in good agreement with the certified values of methylmercury concentration in both materials. The detection limit was 10 microg kg(-1) and the relative standard deviation was < 8% for methylmercury concentrations ranging from 0.15 to 3.0 mg kg(-1).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".