Detection of Adenosine Triphosphate with an Aptamer Biosensor Based on Surface-Enhanced Raman Scattering
Bibliographic record
Abstract
A simple, ultrasensitive, highly selective, and reagent-free aptamer-based biosensor has been developed for quantitative detection of adenosine triphosphate (ATP) using surface-enhanced Raman scattering (SERS). The sensor contains a SERS probe made of gold nanostar@Raman label@SiO(2) core-shell nanoparticles in which the Raman label (malachite green isothiocyanate, MGITC) molecules are sandwiched between a gold nanostar core and a thin silica shell. Such a SERS probe brings enhanced signal and low background fluorescence, shows good water-solubility and stability, and exhibits no sign of photobleaching. The aptamer labeled with the SERS probe is designed to hybridize with the cDNA on a gold film to form a rigid duplex DNA. In the presence of ATP, the interaction between ATP and the aptamer results in the dissociation of the duplex DNA structure and thereby removal of the SERS probe from the gold film, reducing the Raman signal. The response of the SERS biosensor varies linearly with the logarithmic ATP concentration up to 2.0 nM with a limit of detection of 12.4 pM. Our work has provided an effective method for detection of small molecules with SERS.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".