Improved RP-HPLC separation of Hg<sup>2+</sup>and CH<sub>3</sub>Hg<sup>+</sup>using a mixture of thiol-based mobile phase additives
Bibliographic record
Abstract
Hg(2+) and CH(3)Hg(+) are frequently encountered in the environment either as free ions or complexed with organic matter, such as humic acids. The majority of the reported HPLC-based separations of environmental mercury species, however, separate Hg(2+) from CH(3)Hg(+) in which the former species elutes close to the void volume. To detect mercury-species in environmental waters that may have so far escaped detection, a separation method is needed that sufficiently retains both Hg(2+) and CH(3)Hg(+). One way to develop such a method is to increase the retention of Hg(2+) and CH(3)Hg(+) using existing HPLC separations. We here report on the improvement of a previously reported RP-HPLC-based separation of Hg(2+) and CH(3)Hg(+) that employed a 100 % aqueous mobile phase [10 mM L-cysteine (Cys) in 50 mM phosphate buffer (pH 7.5)]. To increase the retention of Hg(2+), Cys was replaced by the comparatively more hydrophobic N-acetylcysteine (N-Cys). To achieve a compromise between an increased retention of Hg(2+) and its baseline separation from CH(3)Hg(+) in the shortest possible analysis time, the retention behavior of both mercurials was investigated on two RP-HPLC columns with mobile phases that contained mixtures of Cys and N-Cys in which the overall thiol concentration was maintained at 10 mM. An optimal separation of both mercurials could be achieved in ∼540 s using a Gemini C(18) HPLC column (150 × 4.6 mm I.D.) and a mobile phase comprised of 7.5 mM N-Cys and 2.5 Cys in 50 mM phosphate buffer (pH 7.4). Coupling the developed HPLC separation with an inductively coupled plasma mass spectrometer should allow one to detect mercury species other than Hg(2+) and CH(3)Hg(+) in environmental waters. The detection of such species is critical to better understand the mobilization of mercury species from natural and anthropogenic pollution sources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".