Minimalist Design of Wireframe DNA Nanotubes: Tunable Geometry, Size, Chirality, and Dynamics
Bibliographic record
Abstract
DNA nanotubes (NTs) have attracted extensive interest as artificial cytoskeletons for biomedical, synthetic biology, and materials applications. Here, we report the modular design and assembly of a minimalist yet robust DNA wireframe nanotube with tunable cross-sectional geometry, cavity size, chirality, and length, while using only four DNA strands. We introduce an h-motif structure incorporating double-crossover (DX) tile-like DNA edges to achieve structural rigidity and provide efficient self-assembly of h-motif-based DNA nanotube (H-NT) units, thus producing programmable, micrometer-long nanotubes. We demonstrate control of the H-NT nanotube length via short DNA modulators. Finally, we use an enzyme, RNase H, to take these structures out of equilibrium and trigger nanotube assembly at a physiologically relevant temperature, underlining future cellular applications. The minimalist H-NTs can assemble at near-physiological salt conditions and will serve as an easily synthesized, DNA-economical modular template for biosensors, plasmonics, or other functional materials and as cost-efficient drug-delivery vehicles for biomedical applications.
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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 | 0.000 |
| 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 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".