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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".