G-Quadruplex DNA for Fluorescent and Colorimetric Detection of Thallium(I)
Bibliographic record
Abstract
Thallium is a highly toxic heavy metal, but its sensing is underexplored compared to its neighboring elements in the periodic table: lead and mercury. Thallium has two oxidation states. A DNAzyme-based biosensor for Tl 3+ was reported recently, representing the first work in this area. However, the most environmentally abundant thallium is monovalent Tl +, which is the focus of this work. Since Tl + is similar to K + in terms of size and charge, G-quadruplex DNAs are herein tested for Tl + detection. First, nine dual fluorophore labeled DNA probes are screened. Among them, a DNA designated PS2.M has the largest increase in fluorescence resonance energy transfer (FRET) efficiency upon Tl + addition. This FRET-based assay is directly used as a biosensor yielding a detection limit of 59 μM Tl + . In comparison, K + had a much lower response and the other tested monovalent metals do not produce a significant signal increase. In addition, a colorimetric sensor was developed based on DNA protected gold nanoparticles. When folded by Tl +, the nonlabeled PS2.M DNA cannot effectively adsorb onto gold nanoparticles. This leads to a color change from red to blue upon salt addition. The detection limit is 4.6 μM Tl +, and Tl + spiked in a lake water sample can also be detected. CD spectroscopy is used to further understand Tl + binding to PS2.M. This study demonstrates that DNA can also be used for detecting Tl +, and this work gives rise to a highly effective probe for this purpose.
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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) | 0.002 | 0.001 |
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".