DNA Aptamer Folding on Gold Nanoparticles: From Colloid Chemistry to Biosensors
Bibliographic record
Abstract
We have investigated the effect of the folding of DNA aptamers on the colloidal stability of gold nanoparticles (AuNPs) to which an aptamer is tethered. On the basis of the studies of two different aptamers (adenosine aptamer and K+ aptamer), we discovered a unique colloidal stabilization effect associated with aptamer folding: AuNPs to which folded aptamer structures are attached are more stable toward salt-induced aggregation than those tethered to unfolded aptamers. This colloidal stabilization effect is more significant when a DNA spacer was incorporated between AuNP and the aptamer or when lower aptamer surface graft densities were used. The conformation that aptamers adopt on the surface appears to be a key factor that determines the relative stability of different AuNPs. Dynamic light scattering experiments revealed that the sizes of AuNPs modified with folded aptamers were larger than those of AuNPs modified with unfolded (but largely collapsed) aptamers in salt solution. From both the electrostatic and steric stabilization points of view, the folded aptamers that are more extended from the surface have a higher stabilization effect on AuNP than the unfolded aptamers. On the basis of this unique phenomenon, colorimetric biosensors have been developed for the detection of adenosine, K+, adenosine deaminase, and its inhibitors. Moreover, distinct AuNP aggregation and redispersion stages can be readily operated by controlling aptamer folding and unfolding states with the addition of adenosine and adenosine deaminase.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".