Mercury Stable Isotope Fractionation during Abiotic Dark Oxidation in the Presence of Thiols and Natural Organic Matter
Bibliographic record
Abstract
Abstract Mercury (Hg) stable isotope fractionation has been widely used to trace Hg sources and transformations in the environment, although many important fractionation processes remain unknown. Here, we describe Hg isotope fractionation during the abiotic dark oxidation of dissolved elemental Hg(0) in the presence of thiol compounds and natural humic acid. We observe equilibrium mass-dependent fractionation (MDF) with enrichment of heavier isotopes in the oxidized Hg(II) and a small negative mass-independent fractionation (MIF) owing to nuclear volume effects. The measured enrichment factors for MDF and MIF (ε202Hg and E199Hg) ranged from 1.10‰ to 1.56‰ and from −0.16‰ to −0.18‰, respectively, and agreed well with theoretically predicted values for equilibrium fractionation between Hg(0) and thiol-bound Hg(II). We suggest that the observed equilibrium fractionation was likely controlled by isotope exchange between Hg(0) and Hg(II) following the production of the Hg(II)–thiol complex. However, significantly attenuated isotope fractionation was observed during the initial stage of Hg(0) oxidation by humic acid and attributed to the kinetic isotope effect (KIE). This research provides additional experimental constraints on interpreting Hg isotope signatures with important implications for the use of Hg isotope fractionation as a tracer of the Hg biogeochemical cycle.
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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".