Ultrasensitive Visual Fluorescence Detection of Heavy Metal Ions in Water Based on DNA-Functionalized Hydrogels
Bibliographic record
Abstract
Heavy metal contamination of oceans, lakes, and other water resources can occur by both natural and human-related processes. Human exposure to heavy metals such as mercury is known to cause a number of serious health problems. Due to its high toxicity and bioaccumulative properties, the maximum toxic level of mercury in drinking water is set to be 10 nM or 2 parts-per-billion by the US EPA. Therefore, detection of mercury at such a low concentration poses an analytical challenge. While analytical instruments such as ICP-MS are still very widely used for heavy metal analysis, biosensors, are emerging as a cost-effective alternative allowing on-site and real-time detection. We herein describe a protocol for preparing polyacrylamide hydrogel-based biosensors functionalized with a thymine-rich DNA that can effectively detect mercury in water. Detection is achieved by the selective binding of Hg 2+ between two thymine bases inducing a hairpin structure where upon the addition of SYBR Green I dye, green fluorescence is observed. In the absence of Hg 2+ , the addition of the dye results in yellow fluorescence. This hydrogel-based sensor can easily detect 10 nM Hg 2+ using the naked eye, can be regenerated using a simple acid treatment, and can be dried for storage and easily rehydrated. This sensor is also used to detect Hg 2+ from Lake Ontario water samples spiked with mercury. In the case where a cationic gel formulation is used, the background fluorescence can be effectively suppressed to increase sensitivity. The future research directions of using such gels to detect other metal ions and to detect metal ions in ocean water are also discussed.
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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".