Automated Synthesis of Wireframe DNA Nanotubes
Bibliographic record
Abstract
ABSTRACT DNA nanotechnology has revolutionized our ability to position matter at the nanoscale, but the preparation of DNA-based architectures remains laborious. To facilitate the formation of custom structures, we report a fully automated method to produce sequence- and size-defined DNA nanotubes. By programming the sequential addition of desired building blocks, rigid DX-tile-based DNA nanotubes (and flexible wireframe DNA structures) are attained, where the total number of possible constructs increases as a power function of the number of different units available. Using single-molecule fluorescence imaging, the kinetics and yield of each synthetic step can be quantitatively determined, revealing differences in self-assembly dynamics as the nanotube is built up from the solid support and providing new insights into DNA self-assembly. The exploitation of automation for both assembly and analysis (tthrough an ad-hoc developed K-means clustering algorithm) facilitates a workflow wherein the synthesis parameters may be iteratively improved upon, demonstrating how a single-molecule ‘assembly-analysis-optimization’ sequence can be used to generate complex, non-covalent materials in good yield. The presented synthetic strategy is generalizable, making use of equipment already available in most standard laboratories and represents the first fully automated supramolecular assembly on a solid support.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".