Toward Automated DNA Nanoprinting: Advancing the Synthesis of Covalently Branched DNA
Bibliographic record
Abstract
Covalently branched DNA molecules are hybrid structures where a small molecule core is covalently linked to different DNA strands. They merge the programmability of DNA nanotechnology with synthetic molecules' functionality, offering enhanced stability over their non-covalent counterparts like double-crossover tiles. They enable the efficient assembly of stable DNA nanostructures with new geometries and functionalities. These motifs can be prepared through "DNA printing", which uses a DNA nanostructure as a temporary template to covalently transfer specific DNA strands to a small molecule core. Here, the "printing" process is streamlined with DNA-immobilized polystyrene microspheres, laying the foundation for future automated DNA printing devices. First, the DNA template hybridizes with reactive complementary strands, which are then crosslinked using a small molecule. Second, beads with fully complementary molecules capture the "daughter" products by strand displacement. This ensures high product yields and high recovery of the "mother" template for reuse. This method allows the precise transfer of different DNA strands onto various small molecules, including aromatics and functional porphyrins. Notably, these branching motifs exhibit remarkable stability toward nucleases without any specialized modifications. Moreover, they can serve as robust building blocks for precise assembly of 3D structures, such as an addressable tetrahedron from only two components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".