A novel approach to cold vapor generation for the determination of mercury in biological samples
Bibliographic record
Abstract
A novel approach to the generation of mercury vapor at high pH by the simple addition of NaOH or other base to a solution containing mercury ions is described. Subsequent addition of another reducing agent, such as NaBH4 or SnCl2, is unnecessary. Following total dissolution of sample using HNO3 and H2O2 in a closed microwave vessel, the pH of the sample solution is increased to 13 by the addition of NaOH. After standing for 90 min in a closed vessel, the Hg0 is directed to the plasma by a flow of argon. Emission from mercury is measured at 253.652 nm by an axial view inductively coupled plasma optical emission spectrometer (ICP OES). The procedure was applied to five certified biological samples, yielding a detection limit (3s, n = 10) of 0.04 µg g-1 based on a nominal sample mass of 0.5 g in a final volume of 50 mL. Calibration was achieved using simple aqueous standard solutions containing 0.1 mol L-1 NaOH. The procedure was efficient, with determined values lying in the range of 85-113 % of the certified values, showing good agreement at the 95% confidence level (t-test). The precision was fit for purpose, with relative standard deviations ranging from 7 to 9%. Organomercury species in solution were not detected; only Hg2+ in solution produces a signal. This new procedure provides for a simple approach to quantitation (and potentially speciation).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".