DNA‐Directed Seeded Synthesis of Gold Nanoparticles without Changing DNA Sequence
Bibliographic record
Abstract
Abstract DNA has been used for directing the growth of noble metal nanoparticles into different morphologies. Most previous studies focused on the effect of DNA sequence, while the effect of DNA adsorption was not thoroughly explored. In this work, we controlled the seeded growth of AuNPs by using the same DNA sequence but under different initial adsorption conditions: room temperature and heating. DNA adsorbed by heating induced more anisotropic nanoparticle growth, and the most effect was observed with 100 nM C30 DNA, where nanoflowers were obtained for the heated sample. By measuring DNA adsorption and desorption, heating did not increase DNA adsorption density but increased the adsorption affinity. The percentage of adsorbed DNA before the growth was only about 10%, regardless of heating, while after the growth, the associated DNA reached 75% or more, indicating that the free DNA also influenced the growth. This study offers fundamental insights into the effect of DNA adsorption on seeded AuNP growth, providing a method to tune the morphology of nanoparticles without changing DNA sequence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".