Electrochemical DNAzyme Sensor for Lead Based on Amplification of DNA−Au Bio-Bar Codes
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".