Sensitive Detection of ssDNA Using an LRET-Based Upconverting Nanohybrid Material
Bibliographic record
Abstract
Water-dispersible, optical hybrid nanoparticles are preferred materials for DNA biosensing due to their biocompatibility. Upconverting nanoparticles are highly desirable optical probes in sensors and bioimaging owing to their sharp emission intensity in the visible region. We herein report a highly sensitive ss-DNA detection based on an energy transfer system that uses a nanohybrid material synthesized by doping NaYF4:Tm(3+)/Yb(3+) upconverting nanoparticles (UCNPs) on silica coated polystyrene-co-acrylic acid (PSA) nanoparticles (PSA/SiO2) as the donor, and gold nanoparticles (AuNPs) decorated with Ir(III) complex as the acceptor. UCNPs tagged on PSA/SiO2 and the cyclometalated Ir(III)/AuNP conjugates were then linked through the ss-DNA sequence. Sequential addition of the target DNA to the probe molecular beacon complex resulted in the separation of the optical nanohybrid material and the quencher, leading to a measurable increase in the blue fluorescence emission intensity. Our results have shown a linear relationship between the fluorescence intensity and target DNA concentration down to the picomolar.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".