Assembly of DNA-Functionalized Gold Nanoparticles with Gaps and Overhangs in Linker DNA
Bibliographic record
Abstract
DNA-directed assembly of gold nanoparticles (AuNPs) has been extensively studied because of its important applications in analytical chemistry, materials science, and nanomedicine. In a typical system, two DNA-functionalized AuNPs are assembled via a linker DNA to form large aggregates. In the majority of the previous reports, the linker DNA is fully base paired with no gaps or overhangs present. Introducing such nonbase-paired regions in the linker DNA has been recently shown to be important for making stimuli-responsive materials and in crystallization of such AuNPs. In this work, we systematically studied the effect of introducing gaps and overhangs in the linker DNA to understand the kinetics of assembly and the melting transition of these aggregates. We found that the assembly kinetics decreased with increasing linker DNA length. The melting temperature decreased with the loss of base stacking by introducing gaps as well as the steric effect of overhangs. Additional insights were obtained by measuring the melting curves of the free DNAs in the absence of AuNPs. For example, it appeared that DNA base stacking at the nick site was favored in assembled nanoparticles compared to that in free DNA. Our results indicate that, while it is possible to form AuNP assemblies with linker DNAs containing various types of unpaired regions, these kinetic and thermodynamic factors need to be considered when designing related sensors and materials.
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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) | 0.000 | 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 | 0.001 | 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 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".