DNA-mediated patterning of gold nanoparticles into discrete structures: modularity, write/erase, and structural switching
Bibliographic record
Abstract
Nanoparticle assemblies hold great promise as new materials in catalysis, nanoelectronic and nanophotonic applications. Many of their properties, which depend on the relative arrangement of the individual nanoparticles within the assembly, are not sufficiently well-understood because of a lack of methods to systematically assemble them into well-defined discrete model systems. In here we discuss a method for the ready access to a large number of discrete nanoparticle assemblies using a small number of single-stranded and cyclic DNA templates that are also dynamic. A triangular template and a square template are used to generate gold nanoparticle assemblies with geometrical control by the simple tagging of each particle to be organized with a DNA sequence that serves to dictate its final position within the construct. The same triangular template is used to access all the possible triangular combinations that two gold nanoparticles of different sizes may be organized in (i.e. three larger, two larger / one smaller, one larger/ two smaller, all smaller). The same square template is used to generate nanoparticle assemblies in which four gold nanoparticles are organized into square, trapezoidal and rectangular arrangements. Post-assembly addressability is demonstrated by a write/erase experiment in which three gold nanoparticles of a single size are assembled into a triangular arrangement, a specific particle is erased using an external eraser strand, and the empty position is re-written with a smaller sized particle. Our approach could be generalized to easily generate large sets of nanoparticle groupings with control over the position, size, type and addressability of each nanoparticle within the construct.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".