Aptamer-Modified Ultrastable Gold Nanoparticles for Dopamine Detection
Bibliographic record
Abstract
Metallic nanoparticles, in particular silver and gold nanoparticles, have been used in various fields for centuries. In the last few decades, their plasmon resonance has made them particularly attractive to biochemists because of their unparalleled optical properties, making them ideal probes for molecular detection. In this article, a new approach to dopamine detection based on ultrastable gold nanoparticles is presented. A dopamine-binding aptamer was used to modify ultrastable gold nanoparticles for the sensitive and selective detection of different concentrations of dopamine without inducing gold nanoparticles aggregation. Indeed, when dopamine binds to the aptamer present at the surface of the gold nanoparticle, the latter exhibits a plasmon shift relative to the dopamine concentration, allowing measurement of its dosage. Besides that, the target molecules can be filtered out to permit nanoparticles reuse. The detection assay showed good linearity between the dopamine concentration and gold nanoparticles' plasmon shift while common interfering molecules, such as ascorbic acid and tyramine, induced no or little plasmon shift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".