Hg<sup>2+</sup> detection using a phosphorothioate RNA probe adsorbed on graphene oxide and a comparison with thymine-rich DNA
Bibliographic record
Abstract
Mercury is a highly toxic heavy metal and many DNA-based biosensors have been recently developed for Hg(2+) detection in water. Among them, thymine-rich DNA is the most commonly used for designing Hg(2+) sensors. However, the thymine-Hg(2+) interaction is strongly affected by the buffer conditions. We recently reported a molecular beacon containing phosphorothioate (PS)-modified RNA linkages that can be cleaved by Hg(2+). In this work, the fluorescence quenching and DNA adsorption properties of nano-sized graphene oxide (NGO) were used to develop a new sensor using the PS-RNA chemistry. Three DNA probes, containing one, three and five PS-RNA linkages, respectively, were tested. Finally, a fluorophore-labeled poly-A DNA with five PS-RNA linkages was selected and adsorbed by NGO. In the presence of Hg(2+), the fluorophore was released from NGO due to the cleavage reaction, resulting in a fluorescence enhancement. This sensor is highly selective for Hg(2+) with a detection limit of 8.5 nM Hg(2+). For comparison, a fluorophore-labeled poly-T DNA was also tested, which responded to Hg(2+) more slowly and was inhibited by high NaCl concentrations, while the PS-RNA probe was more tolerant to different buffer conditions. This work indicates a new method for interfacing DNA with NGO for Hg(2+) detection.
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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 | 0.000 | 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".