Isotope Fractionation of Mercury during Its Photochemical Reduction by Low-Molecular-Weight Organic Compounds
Bibliographic record
Abstract
Photochemical reduction of Hg(II) by various low-molecular-weight organic compounds (LMWOC) was investigated to evaluate the effect of specific functional groups that are typically encountered in natural dissolved organic matters (DOM) on the photoreactivity and isotope fractionation of Hg. LMWOC with reduced sulfur functional groups (e.g., cysteine, glutathione) resulted in slower photochemical reduction of Hg(II) than those without reduced sulfur groups (e.g., serine, oxalic acid). Reduction rate constants were specifically determined for two contrasting LMWOC: dl-serine (0.640 h(-1)) and l-cysteine (0.047 h(-1)). Different mass independent isotope effects of Hg were induced by the two types of LMWOC. S-containing ligands specifically enriched magnetic isotopes ((199)Hg and (201)Hg) in the product (Hg(0)) while sulfurless ligands enriched (199)Hg and (201)Hg in the reactant (Hg(II)), suggesting that opposite magnetic isotope effects were produced by different types of ligands. The nuclear field shift effect was also observed in the photochemical reduction by serine. These isotope effects are related to specific functional groups and reduction mechanisms, and may be used to distinguish between primary and secondary photochemical reduction mechanisms of Hg(II) and to explain isotope fractionation during the photochemical reduction of Hg(II) by natural DOM, which provides mixed bonding conditions.
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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".