Speciation and mobilization of ultra-trace Hg(II) in groundwater
Bibliographic record
Abstract
Accurate quantification of various mercury (Hg) species dynamics in groundwater is critical for understanding Hg mobilization, fate, and consequent impacts on water ecological security. This foundational work, however, faces challenges due to the lack of highly sensitive, reliable, and field-deployable detection technologies that can determine and monitor ultra-trace Hg(II) in groundwater. Here, this research presents and assesses two types of biosensing methods for dissolved Hg(II) based on a deoxyribonucleic acid (DNA) sensing material: the DNA-functionalized hydrogel for direct Hg(II) detection in groundwater and the DNA-DGT sensor for simultaneous sampling and detection with the diffusive gradients in thin films technique (DGT). Applying tests to hydrogeochemically diverse groundwaters from the Grand River Watershed, Canada, the results indicate that the DNA-functionalized hydrogel is able to quickly detect dissolved Hg(II) but inapplicable to low Hg(II) concentrations (<1.60 μg/L), whereas the DNA-DGT sensor can capture variably ultra-trace Hg(II) species depending on the deployment time. Quantification of Hg(II) species in groundwater via joint DNA-DGT sensing and hydrogeochemical calculation indicates that temperature, pH, Cl−, and dissolved organic matter significantly affected partitioning of trace Hg(II) between various mobile species, diffusion efficiency, and thus its mobility. Combined with hydrogeochemical modeling, the DNA-DGT measurements reveal that mobilization and transformation of Hg(II) are linked to redox cycling of sulfur in groundwater. This study therefore highlights that monitoring of low-level Hg(II) with ultra-sensitive, field-deployable biosensing methods is of significance to understanding mobility and fate of Hg in groundwater and its threat to safe drinking water supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".