Bibliographic record
Abstract
A molecular beacon (MB) is comprised of a fluorophore and a quencher linked by a DNA hairpin. MBs have been widely used for homogeneous DNA detection. In addition to molecular quenchers, many nanomaterials such as graphene oxide (GO) also possess excellent quenching efficiency. Most reported fluorescent sensors relied on DNA probes physisorbed by GO, which may suffer from nonspecific probe displacement and false positive signal. In this work, we report the preparation and characterization of a MB using graphene oxide (GO) as quencher, where an amino and FAM (6-carboxyfluorescein) dual labeled DNA was covalently attached to GO via an amide linkage. A major challenge was to remove noncovalently attached probes due to strong DNA adsorption by GO. While DNA desorption was favored at low salt, high pH, high temperature, and by using organic solvents, the cDNA was required to achieve complete desorption of noncovalently linked DNA probes. The DNA adsorption energy was measured using isothermal titration calorimetry, revealing the heterogeneous nature of GO. The covalent probe has a detection limit of 2.2 nM using a sample volume of 0.05 mL. With a 2 mL sample, the detection limit can reach 150 pM. The covalent probe is highly resistant to nonspecific probe displacement and will find applications in serum and cellular samples where high probe stability is demanded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".