The assemble, grow and lift-off (AGLO) strategy to construct complex gold nanostructures with pre-designed morphologies
Bibliographic record
Abstract
The construction of metallic nanostructures with customizable morphologies and complex shapes has been an essential pursuit in nanoscience. DNA nanotechnology has enabled the fabrication of increasingly complex DNA nanostructures with unprecedented specificity, programmability and sub-nanometer precision, which makes it an ideal approach to rationally organize metallic nanostructures. Here we report an Assemble, Grow and Lift-Off (AGLO) strategy to construct robust standalone gold nanostructures with pre-designed customizable shapes in solution, using only a simple 2D DNA origami sheet as a versatile transient template. Gold nanoparticle (AuNP) seeds were firstly assembled onto the pre-designed binding sites of the DNA origami template and then additional gold was slowly deposited onto the AuNP seeds. The growing seed surfaces eventually merge with adjacent seeds to generate one continuous gold nanostructure in a pre-designed shape, which can then be lifted off the origami template. Diverse customized patterns of templated AuNP seeds were successfully transformed into corresponding gold nanostructures with the target structure transformation percentage over 80%. Moreover, the AGLO strategy can be incorporated with a magnetic bead separation platform to enable the easy recycling of the excess AuNP seeds and DNA components.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".