Long-Range Assembly of DNA into Nanofibers and Highly Ordered Networks Using a Block Copolymer Approach
Bibliographic record
Abstract
A simple method to introduce the long-range order achieved by block copolymers into DNA structures is described. This results in the hierarchical assembly of short DNA strands into a new one-dimensional material, with high aspect ratio and the ability to further align into highly ordered surfaces over tens of micrometers. Fibers derived from biological materials have a wide range of potential applications, such as scaffolds for nanowires and one-dimensional (1D) materials, templates for tissue growth, and ligand display tools for multivalent biological interactions. Fibers derived from short DNA strands are an attractive class of materials, as they combine long-range 1D ordering with the programmability of DNA, and its ability to undergo structure switching with specifically added DNA strands. Here, we present the first examples of long fibers self-assembled from short (10-20 base-pairs), blunt-ended DNA strands. This was accomplished by covalently attaching a dendritic oligoethylene glycol (OEG) unit to a DNA strand to form a dendritic DNA molecule (D-DNA). Hybridization of this unit with complementary DNA creates a block copolymer/double-stranded DNA architecture, which readily undergoes self-assembly into long fibers upon the addition of a selective solvent. These fibers can further align into parallel rows, to yield highly ordered micrometer-sized surfaces. We demonstrate that a DNA nanotechnology motif, a three-helix DNA bundle, can also be readily induced to form long fibers upon incorporation of D-DNA. Thus, this provides a straightforward method to introduce hierarchical long-range ordering into DNA motifs, simply through hybridization with short D-DNA strands.
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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".