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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".