Determination of mercury by continuous flow cold vapor atomic fluorescence spectrometry using micromolar concentration of sodium tetrahydroborate as reductant solution
Bibliographic record
Abstract
Systematic experiments were conducted to evaluate and compare the analytical figures of merit of two reducing agents (SnCl2 and NaBH4) in a continuous flow cold vapor atomic fluorescence mercury analyzer. It was found that sodium tetrahydroborate can efficiently reduce Hg2+ in various environmental samples at a concentration as low as 10 microM (ca. 3.8 x 10(-5)% w/v). Most commonly encountered transition metals (Fe2+, Fe3+, Zn2+, Cu2+, Ni2+, Pb2+ and Cr3+) did not interfere with total Hg determination. No interference from hydride-forming elements (Se4+, Sb3+ and As3+) was observed. Interference caused by Mn2+ and Ag+ could be readily removed by dilution and by using appropriate modification of the reaction matrix. A higher concentration of NaBH4 (0.1 M) is stable for I month when stored in the NaOH matrix (0.2 M) and at low temperature (4 degrees C). A working solution of NaBH4 can be freshly prepared by dilution. With NaBH4, the whole continuous flow system is kept clean much more easily as no precipitate is formed, which in turn considerably reduces memory effects, simplifies analytical operation and reduces the chemical cost six-fold.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".