Minimalist Approach to Complexity: Templating the Assembly of DNA Tile Structures with Sequentially Grown Input Strands
Bibliographic record
Abstract
Given its highly predictable self-assembly properties, DNA has proven to be an excellent template toward the design of functional materials. Prominent examples include the remarkable complexity provided by DNA origami and single-stranded tile (SST) assemblies, which require hundreds of unique component strands. However, in many cases, the majority of the DNA assembly is purely structural, and only a small "working area" needs to be aperiodic. On the other hand, extended lattices formed by DNA tile motifs require only a few strands; but they suffer from lack of size control and limited periodic patterning. To overcome these limitations, we adopt a templation strategy, where an input strand of DNA dictates the size and patterning of resultant DNA tile structures. To prepare these templating input strands, a sequential growth technique developed in our lab is used, whereby extended DNA strands of defined sequence and length may be generated simply by controlling their order of addition. With these, we demonstrate the periodic patterning of size-controlled double-crossover (DX) and triple-crossover (TX) tile structures, as well as intentionally designed aperiodicity of a DX tile structure. As such, we are able to prepare size-controlled DNA structures featuring aperiodicity only where necessary with exceptional economy and efficiency.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".