Isolation and Identification of Mercury–Dissolved Organic Matter Complexes in Mercury–Humic Acid Suspensions
Bibliographic record
Abstract
ABSTRACT Rationale The complexation with dissolved organic matter (DOM) is a pivotal factor influencing transformations, transport, and bioavailability of mercury (Hg) in aquatic environments. However, identifying these complexes poses a significant challenge because of their low concentrations and the presence of coexisting ions. Methods In this study, mercury–dissolved organic matter (Hg‐DOM) complexes were isolated through solid‐phase extraction (SPE) from Hg–humic acid suspensions, and complexes were putatively identified using ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). Results Dissolved organic carbon (DOC) and total Hg analysis before and after SPE showed an increase in DOC:Hg ratio. The DOC:Hg ratio was lower in extracts from cartridges with silica structure bonded with hydrocarbon chains (C18) than priority pollutant (PPL) cartridges at circumneutral pH, indicating that C18 was more effective at extracting DOM complexed Hg. These results were confirmed with FTICR‐MS analysis, where two Hg‐DOM complexes were putatively identified from PPL extracts as opposed to eight from C18 (Winnow score > 75%). In addition, C 8 H 13 HgN 2 O 2 S, a molecular formula with a m/z ratio of 403.04, was identified across three separate extractions using a C18 cartridge, suggesting that the complexes were preserved during extraction and, presumably, electrospray ionization. Conclusions The results highlight the effectiveness of the methodology developed in this study—SPE coupled with FTICR‐MS for isolating and identifying Hg‐DOM complexes. This approach allows for the exploration of the elemental and structural composition of Hg‐DOM complexes, which affects Hg speciation, bioavailability, and transformations in aquatic ecosystems. Synopsis A methodology was developed to identify Hg‐DOM complexes at low concentrations to gain insight into mercury bioavailability, transformations, and transport in the environment.
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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".