Automated Synthesis of DNA Nanostructures
Bibliographic record
Abstract
DNA nanotechnology has revolutionized the ability to position matter at the nanoscale, but the preparation of DNA-based architectures remains laborious. To facilitate the formation of custom structures, a fully automated method is reported 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 (through 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, noncovalent 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".