Electrochemical DNAzyme Sensor for Lead Based on Amplification of DNA−Au Bio-Bar Codes
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Bibliographic record
Abstract
An electrochemical DNAzyme sensor for sensitive and selective detection of lead ion (Pb(2+)) has been developed, taking advantage of catalytic reactions of a DNAzyme upon its binding to Pb(2+) and the use of DNA-Au bio-bar codes to achieve signal enhancement. A specific DNAzyme for Pb(2+) is immobilized onto an Au electrode surface via a thiol-Au interaction. The DNAzyme hybridizes to a specially designed complementary substrate strand that has an overhang, which in turn hybridizes to the DNA-Au bio-bar code (short oligonucleotides attached to 13 nm gold nanoparticles). A redox mediator, Ru(NH3)6(3+), which can bind to the anionic phosphate of DNA through electrostatic interactions, serves as the electrochemical signal transducer. Upon binding of Pb(2+) to the DNAzyme, the DNAzyme catalyzes the hydrolytic cleavage of the substrate, resulting in the removal of the substrate strand along with the DNA bio-bar code and the bound Ru(NH3)6(3+) from the Au electrode surface. The release of Ru(NH3)6(3+) results in lower electrochemical signal of Ru(NH3)6(3+) confined on the electrode surface. Differential pulse voltammetry (DPV) signals of Ru(NH3)6(3+) provides quantitative measures of the concentrations of Pb(2+), with a linear calibration ranging from 5 nM to 0.1 microM. Because each nanoparticle carries a large number of DNA strands that bind to the signal transducer molecule Ru(NH3)6(3+), the use of DNA-Au bio-bar codes enhances the detection sensitivity by five times, enabling the detection of Pb(2+) at a very low level (1 nM). The DPV signal response of the DNAzyme sensor is negligible for other divalent metal ions, indicating that the sensor is highly selective for Pb(2+). Although this DNAzyme sensor is demonstrated for the detection of Pb(2+), it has the potential to serve as a general platform for design sensors for other small molecules and heavy metal ions.
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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 it