Dimethylmercury in natural waters—analytical and experimental considerations
Bibliographic record
Abstract
Abstract Mono‐ and dimethylmercury (MMHg and DMHg, respectively) are the two primary organic forms of mercury (Hg) found in natural waters. While experimental approaches to characterize the environmental behavior of MMHg and inorganic forms of Hg are widely used today, few laboratories conduct experimental studies entailing the use of DMHg. In this paper, we have evaluated and developed different analytical and experimental approaches to quantify and use DMHg in laboratory studies. We demonstrate that DMHg can be analyzed from samples where MMHg is derivatized using sodium tetraethyl borate and where the matrix effects of dissolved sulfide are masked using copper sulfate. Tests, where the calibration curves of MMHg and DMHg were used, showed that MMHg may be used to calibrate for DMHg. For the pre‐concentration of DMHg, both traps filled with Tenax® TA and Bond Elut ENV were found suitable. We observed good recoveries of DMHg added to different types of natural waters or purified water containing aquarium salt, sodium chloride and dissolved sulfide, iron sulfide, and cadmium sulfide at DMHg : sulfide molar ratios > 10−6. In addition to evaluating these analytical aspects, we present suitable subsampling techniques for DMHg‐containing solutions, the recovery of DMHg when filtering DMHg through different types of filters, and experimental data on the long‐term stability of DMHg added to different types of waters and stored at different temperatures. Finally, we present and discuss a new synthetization protocol for preparing aqueous solutions containing DMHg free of organic solvents and where handling DMHg in a pure form is prevented.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".