Reversible and Irreversible HAuCl<sub>4</sub> Binding to DNA for Seeded Gold Nanoparticle Growth and Opposite DNA and Aptamers Colorimetric Sensing Outcomes
Bibliographic record
Abstract
Abstract DNA‐directed seeded growth of gold nanoparticles has been used for the development of aptamer‐based biosensors with the assumption that target analytes can modulate the adsorption of aptamers to the gold seeds and thus affect the growth reaction. To understand the reaction, the effect of single‐ and double‐stranded DNA is first examined, and it is found that they have a similar promotion effect of anisotropic growth, suggesting that DNA cannot be detected using this method. By studying the interaction between HAuCl4 and DNA, both weak reversible binding and strong irreversible binding are identified, with the latter becoming dominating with a longer incubation time. Single‐ and double‐stranded DNA have similar weak binding to HAuCl4, and this weak binding is more important for the growth reaction. Then three aptamers are tested, where only cortisol appeared to modulate its aptamer adsorption and the growth reaction reflected aptamer binding. Hg2+ shows no advantage for its aptamer, and quinine induced aggregation of AuNPs cannot be detected by this reaction either. Therefore, each aptamer target needs to be individually studied to test if this method is applicable. It is also noted that DNA and aptamers have opposite outcomes for the target‐dependent growth reactions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".