Instantaneous Attachment of an Ultrahigh Density of Nonthiolated DNA to Gold Nanoparticles and Its Applications
Bibliographic record
Abstract
The last 16 years have witnessed the landmark development of polyvalent thiolated DNA-functionalized gold nanoparticles (AuNP's) possessing striking properties within the emerging field of nanobiotechnology. Many novel properties of this hybrid nanomaterial are attributed to the dense DNA shell. However, the question of whether nonthiolated polyvalent DNA-AuNP could be fabricated with a high DNA density and properties similar to those of its thiolated counterpart has not been explored in detail. Herein, we report that by simply tuning the pH of the DNA-AuNP mixture an ultrahigh capacity of nonthiolated DNA can be conjugated to AuNP's in a few minutes, resulting in polyvalent DNA-AuNP conjugates with cooperative melting behavior, a typical property of polyvalent thiolated DNA-functionalized AuNP's. With this method, large AuNP's (e.g., 50 nm) can be functionalized to achieve the colorimetric detection of sub-nanometer DNA. Furthermore, this fast, stable DNA loading was employed to separate AuNP's of different sizes. We propose that a large fraction of the attached DNAs are adsorbed via one or a few terminal bases to afford the high loading capacity and the ability to hybridize with the complementary DNA. This discovery not only offers a time- and cost-effective way to functionalize AuNP's with a high density of nonthiolated DNA but also provides new insights into the fundamental understanding of how DNA strands with different sequences interact with AuNP's.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".