Electrostatically Directed Visual Fluorescence Response of DNA-Functionalized Monolithic Hydrogels for Highly Sensitive Hg<sup>2+</sup> Detection
Bibliographic record
Abstract
Hydrogels are cross-linked hydrophilic polymer networks with low optical background and high loading capacity for immobilization of biomolecules. Importantly, the property of hydrogel can be precisely controlled by changing the monomer composition. This feature, however, has not been investigated in the rational design of hydrogel-based optical sensors. We herein explore electrostatic interactions between an immobilized mercury binding DNA, a DNA staining dye (SYBR Green I), and the hydrogel backbone. A thymine-rich DNA was covalently functionalized within monolithic hydrogels containing a positive, neutral, or negative backbone. These hydrogels can be used as sensors for mercury detection since the DNA can selectively bind Hg(2+) between thymine bases inducing a hairpin structure. SYBR Green I can then bind to the hairpin to emit green fluorescence. For the neutral or negatively charged gels, addition of the dye in the absence of Hg(2+) resulted in intense yellow background fluorescence, which was attributed to SYBR Green I binding to the unfolded DNA. We found that, by introducing 20% positively charged allylamine monomer, the background fluorescence was significantly reduced. This was attributed to the repulsion between positively charged SYBR Green I by the gel matrix as well as the strong binding between the DNA and the gel backbone. The signal-to-background ratio and detection limit was, respectively, improved by 6- and 9-fold using the cationic gel instead of neutral polyacrylamide gel. This study helps understand the electrostatic interaction within hydrogels, showing that hydrogels can not only serve as a high capacity matrix for sensor immobilization but also can actively influence the interaction between involved molecules.
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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 | 0.001 | 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) | 0.000 | 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".