Visual detection oflead(II) using a label-free DNA-based sensor and its immobilization within a monolithic hydrogel
Bibliographic record
Abstract
Lead is highly toxic and its detection has attracted a lot of research interests. In recent years, DNA has been used for Pb(2+) recognition and many fluorescent sensors with low to sub-nM detection limits have been reported. These figures of merit were typically measured using a spectrophotometer that can detect nM DNA with a high signal-to-noise ratio. For visual detection, however, μM DNA or dye was required, making it difficult to detect low nM targets. We recently achieved a visual sensitivity of 10 nM Hg(2+) by immobilizing a DNA probe in a hydrogel. This was made possible because the gel was able to actively adsorb Hg(2+). In this work, we aim to test whether this method can be extended to the detection of Pb(2+). First, a new Pb(2+) sensor was designed based on a guanine-rich DNA and DNA binding dyes such as thiazole orange and SYBR Green I. The free DNA showed a detection limit of 8 nM Pb(2+) using 40 nM DNA. For visual detection in solution with 1 μM of the DNA probe, however, ∼300 nM Pb(2+) was required. After immobilization in a monolithic polyacrylamide hydrogel, even 20 nM Pb(2+) could be visually detected with a sample volume of 50 mL. Therefore, sensitive detection without signal amplification was achieved. Finally, we demonstrated simultaneous detection of both Hg(2+) and Pb(2+) in the same water sample with shape encoded hydrogel sensors.
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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".