Label‐Free Colorimetric Biosensors Based on Aptamers and Gold Nanoparticles: A Critical Review
Bibliographic record
Abstract
Abstract Taking advantage of the adsorption of single‐stranded DNA oligonucleotides by gold nanoparticles (AuNPs) and the protection effect of the adsorbed DNA against salt‐induced aggregation of AuNPs, a label‐free colorimetric sensor for the detection of DNA was reported in 2004. Since then, the range of target molecules has extended from complementary nucleic acids to metal ions and small molecules by using aptamers. In the presence of target molecules, a blue color arising from aggregated AuNPs is expected. However, these sensors only considered aptamer binding to its target, and the adsorption of aptamers by AuNPs, while the target/AuNP interactions were ignored. We recently found that target adsorption can often dominate the system. In this Review, we list literature examples of using this label‐free strategy for sensing aptamer targets. Seven target analytes are discussed in detail. For As(III), dopamine, melamine, kanamycin, adenosine, and ATP, target adsorption dominated, and the same color change was observed even with non‐aptamer sequences. Only in the case of K + detection, did the effect of specific aptamer binding dominate, attributable to weak K + /AuNP interactions. These examples call for a careful evaluation of target adsorption and the use of non‐aptamer control sequences in validating these sensors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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".