Formation of Hg(II) tetrathiolate complexes with cysteine at neutral pH
Bibliographic record
Abstract
Mercury(II) ions precipitate from aqueous cysteine (H2Cys) solutions containing H2Cys/Hg(II) mole ratio ≥ 2.0 as Hg(S-HCys)2. In the absence of additional cysteine, the precipitate dissolves at pH ∼12 with the [Hg(S,N-Cys)2]2– complex dominating. With excess cysteine (H2Cys/Hg(II) mole ratio ≥ 4.0), higher complexes form and the precipitate dissolves at lower pH values. Previously, we found that tetrathiolate [Hg(S-Cys)4]6– complexes form at pH = 11.0; in this work, we extend the investigation to pH values of physiological interest. We examined two series of Hg(II)–cysteine solutions in which CHg(II) varied between 8 and 9 mmol/L and 80 and 100 mmol/L, respectively, with H2Cys/Hg(II) mole ratios from 4 to ∼20. The solutions were prepared in the pH range 7.1–8.8 at the pH at which the initial Hg(S-HCys)2 precipitate dissolved. The variations in the Hg(II) speciation were followed by 199Hg NMR, X-ray absorption, and Raman spectroscopic techniques. Our results show that in the dilute solutions (CHg(II) = 8–9 mmol/L), mixtures of di-, tri- (major), and tetrathiolate complexes exist at moderate cysteine excess (CH2Cys ∼0.16 mol/L) at pH 7.1. In the more concentrated solutions (CHg(II) = 80–100 mmol/L) with high cysteine excess (CH2Cys > 0.9 mol/L), tetrathiolate [Hg(S-cysteinate)4]m−6 (m = 0–4) complexes dominate in the pH range 7.3–7.8, with lower charge than for the [Hg(S-Cys)4]6– complex due to protonation of some (m) of the amino groups of the coordinated cysteine ligands. The results of this investigation could provide a key to the mechanism of biosorption and accumulation of Hg(II) ions in biological/environmental systems.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".