Tuning Ratios, Densities, and Supramolecular Spacing in Bifunctional DNA‐Modified Gold Nanoparticles
Bibliographic record
Abstract
Methods for combining multiple functions into well-defined nanomaterials are still lacking, despite their need in nanomedicine and within the broader field of nanotechnology. Here several strategies for controlling the amount and the ratio of combinations of labeled DNA on 13-nm gold nanoparticles using self-assembly of thiolated DNA and/or DNA-directed assembly are explored. It is found that the self-assembly of mixtures of fluorescently labeled DNA can lead to a higher amount of labeled DNA per particle; however, the ratio of fluorophores on the nanoparticles differs greatly from that in the self-assembly solution. In contrast, when fluorescently labeled DNA are hybridized to DNA-modified gold nanoparticles, the fluorophore ratio on the nanoparticles is much closer to their ratio in solution. The use of bifunctional DNA-doublers in self-assembly and DNA-directed assembly is also explored to increase the complexity of these materials and control their composition. Finally, tuning the distance between the labels from 2.9 to 5.4 nm was achieved using different hybridized DNA clamp complexes. Fluorescent results suggest that assembling these clamps on nanoparticle surfaces may be possible, although the resulting label spacing could not be quantified.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".