Graphene Oxide-Based Nanostructured DNA Sensor
Bibliographic record
Abstract
Quick detection of DNA sequence is vital for many fields, especially, early-stage diagnosis. Here, we develop a graphene oxide-based fluorescence quenching sensor to quickly and accurately detect small amounts of a single strand of DNA. In this paper, fluorescent magnetic nanoparticles (FMNPs) modified with target DNA sequence (DNA-t) were bound onto the modified graphene oxide acting as the fluorescence quenching element. FMNPs are made of iron oxide (Fe3O4) core and fluorescent silica (SiO2) shell. The average particle size of FMNPs was 74 ± 6 nm and the average thickness of the silica shell, estimated from TEM results, was 30 ± 4 nm. The photoluminescence and magnetic properties of FMNPs have been investigated. Target oligonucleotide (DNA-t) was conjugated onto FMNPs through glutaraldehyde crosslinking. Meanwhile, graphene oxide (GO) nanosheets were produced by a modified Hummers method. A complementary oligonucleotide (DNA-c) was designed to interact with GO. In the presence of GO-modified with DNA-c, the fluorescence intensity of FMNPs modified with DNA-t was quenched through a FRET quenching mechanism. Our study indicates that FMNPs can not only act as a FRET donor, but also enhance the sensor accuracy by magnetically separating the sensing system from free DNA and non-hybridized GO. Results indicate that this sensing system is ideal to detect small amounts of DNA-t with limitation detection at 0.12 µM.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".