Dynamic DNA Templates for Discrete Gold Nanoparticle Assemblies: Control of Geometry, Modularity, Write/Erase and Structural Switching
Bibliographic record
Abstract
Nanoparticle assemblies hold great promise as new materials for catalysis, nanoelectronics, and nanophotonics applications. However, many of their properties, which depend on the relative arrangement of the particles within the assembly, are not sufficiently well-understood because of a lack of methods to systematically assemble them into well-defined discrete model systems. We here report a method which uses a minimal set of dynamic DNA templates to generate a large number of discrete gold nanoparticle assemblies. These assemblies are addressable in real time and can undergo structural switching and write/erase functions in response to external agents. More specifically, control of geometry is demonstrated by the facile creation of triangle and square gold nanoparticle assemblies; modularity is shown by positioning two different sizes of gold nanoparticles into all the possible triangular combinations; structural switching is established by the use of the same square template to selectively construct square, trapezoidal, and rectangular assemblies; and a write/erase function is shown by assembling a triangle of three gold nanoparticles, selectively removing one of the particles, followed by the “writing” of a different particle. The study of these systems promises to shed light on the phenomena of single electron transport and optical coupling in nanoparticle assemblies and will lead to the more effective incorporation of nanoparticles in photonic/electronic devices. In principle, our dynamic templates can be used to organize any DNA-labeled nanocomponent into well-defined and addressable structures, and as such, this constitutes a new and economical method to construct discrete nanoparticle materials on the nanoscale.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".