Direct Measurement of Aqueous Mercury(II): Combining DNA-Based Sensing with Diffusive Gradients in Thin Films
Bibliographic record
Abstract
Abstract A highly specific DNA-functionalized hydrogel sensing layer was integrated with the diffusive gradients in thin films (DGT) technique for the direct determination of aqueous mercury(II). The DNA-functionalized layer in the DGT unit exhibited both high affinity (complexation constant Kc = 1019.8 at 25 °C) and high binding capacity (9.5 mg Hg disk–1) toward Hg2+. The diffusion coefficient for Hg2+ complexed with common inorganic ligands was an order of magnitude higher than that for Hg2+ complexed with natural dissolved organic matter: 9.0 × 10–6 versus 9.8 × 10–7 cm2 s–1 at 25 °C. The performance of the DNA-DGT sensor was further assessed under variable pH (3–10) and temperature (5–40 °C) conditions, as well as across a range of hydrochemically diverse artificial and natural freshwaters. The observed effects of the environmental and solution compositional variables on Hg2+ binding to the DNA in the sensing layer were successfully accounted for by equilibrium speciation calculations and temperature-corrected, multicomponent diffusion coefficients for aqueous Hg(II). The results therefore support the use of the DNA-DGT sensor as an alternative to traditional sampling and analysis methods for measuring aqueous Hg(II) concentrations down to the nanomolar level in freshwater environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".