Multi-micron crisscross structures from combinatorially assembled DNA-origami slats
Bibliographic record
Abstract
Abstract Living systems achieve robust self-assembly across length scales. Meanwhile, nanofabrication strategies such as DNA origami have enabled robust self-assembly of submicron-scale shapes.However, erroneous and missing linkages restrict the number of unique origami that can be practically combined into a single supershape. We introduce crisscross polymerization of DNA-origami slats for strictly seed-dependent growth of custom multi-micron shapes with user-defined nanoscale surface patterning. Using a library of ~2000 strands that can be combinatorially assembled to yield any of ~1e48 distinct DNA origami slats, we realize five-gigadalton structures composed of >1000 uniquely addressable slats, and periodic structures incorporating >10,000 slats. Thus crisscross growth provides a generalizable route for prototyping and scalable production of devices integrating thousands of unique components that each are sophisticated and molecularly precise. One-sentence summary Crisscross polymerization of DNA-origami slats can yield micron-scale structures with uniquely addressable nanoscale features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".