Mercury Isotope Fractionation during Dark Abiotic Reduction of Hg(II) by Dissolved, Surface-Bound, and Structural Fe(II)
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Stable mercury (Hg) isotope ratios are an emerging tracer for biogeochemical transformations in environmental systems, but their application requires knowledge of isotopic enrichment factors for individual processes. We investigated Hg isotope fractionation during dark, abiotic reduction of Hg(II) by dissolved iron(Fe)(II), magnetite, and Fe(II) sorbed to boehmite or goethite by analyzing both the reactants and products of laboratory experiments. For homogeneous reduction of Hg(II) by dissolved Fe(II) in continuously purged reactors, the results followed a Rayleigh distillation model with enrichment factors of −2.20 ± 0.16‰ (ε 202 Hg) and 0.21 ± 0.02‰ (E 199 Hg). In closed system experiments, allowing reequilibration, the initial kinetic fractionation was overprinted by isotope exchange and followed a linear equilibrium model with −2.44 ± 0.17‰ (ε 202 Hg) and 0.34 ± 0.02‰ (E 199 Hg). Heterogeneous Hg(II) reduction by magnetite caused a smaller isotopic fractionation (−1.38 ± 0.07 and 0.13 ± 0.01‰), whereas the extent of isotopic fractionation of the sorbed Fe(II) experiments was similar to the kinetic homogeneous case. Small mass-independent fractionation of even-mass Hg isotopes with 0.02 ± 0.003‰ (E 200 Hg) and ≈ −0.02 ± 0.01‰ (E 204 Hg) was consistent with theoretical predictions for the nuclear volume effect. This study contributes significantly to the database of Hg isotope enrichment factors for specific processes. Our findings show that Hg(II) reduction by dissolved Fe(II) in open systems results in a kinetic MDF with a larger ε compared to other abiotic reduction pathways, and combining MDF with the observed MIF allows the distinction from photochemical or microbial Hg(II) reduction pathways.
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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".