Dopamine and Melamine Binding to Gold Nanoparticles Dominates Their Aptamer-Based Label-Free Colorimetric Sensing
Bibliographic record
Abstract
Target-directed aptamer adsorption by gold nanoparticles (AuNPs) has been widely used to develop label-free colorimetric biosensors. However, the potential interactions between target molecules and AuNPs have not been considered, which may lead to misinterpretation of analytical results. In this work, the detection of dopamine, melamine, and K + was studied as model systems to address this problem. First, dopamine and two control molecules all induced the aggregation of citrate-capped AuNPs with apparent K d ’s of 5.8 μM dopamine, 51.6 μM norepinephrine, and 142 μM tyramine. Isothermal titration calorimetry measured the aptamer K d to be 1.9 μM dopamine and 16.8 μM norepinephrine, whereas tyramine cannot bind. Surface enhanced Raman spectroscopy confirmed direct adsorption of dopamine, and the adsorbed dopamine inhibited the adsorption of DNA. Using a typical salt-induced colorimetric detection protocol, a similar color response was observed regardless of the sequence of DNA, indicating the observed color change reflected the adsorption of dopamine by the AuNPs instead of the binding of dopamine by the aptamer. For this label-free sensor to work, the interaction between the target molecule and AuNPs should be very weak, while dopamine represents an example of strong interactions. For the other two systems, the melamine detection did not reflect aptamer binding either but the K + detection did, suggesting melamine also strongly interacted with AuNPs, whereas K + had very weak interactions with AuNPs. Since each target molecule is different, such target/AuNP interactions need to be studied case-by-case to ensure the sensing mechanism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".