Templated Synthesis of DNA Nanotubes with Controlled, Predetermined Lengths
Bibliographic record
Abstract
We report a DNA-templated approach to construct nanotubes with controlled lengths and narrow molecular weight distribution, allowing the deliberate variation of this length. This approach relies on the facile and modular assembly of a DNA guide strand of precise length that contains single-stranded gaps repeating at every 50 nm. This is followed by positioning triangular DNA "rungs" on each of these single-stranded gaps and adding identical linking strands to the two other sides of the triangles to close the DNA nanotubes. The length of the guide strand can be deliberately changed. We show the use of this approach to produce nanotubes with lengths of 1 microm or 500 nm and narrow length distributions. This is in contrast to nontemplated approaches, which lead to long and polydisperse nanotubes. We also demonstrate the encapsulation of 20 nm gold nanoparticles within these well-defined nanotubes to form finite lines of gold nanoparticles with longitudinal plasmon coupling, with a number of potential nanophotonic applications. This guiding strand approach is a useful tool in the creation of DNA nanostructures, in this case allowing the use of a simple template generated by a minimal number of DNA strands to program the length and molecular weight distribution of assemblies, as well as to organize any number of DNA-labeled nano-objects into finite structures.
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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) | 0.001 | 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".