Long‐range assembly of DNA into nanofibers and highly ordered networks
Bibliographic record
Abstract
Abstract Long‐range assembly of DNA currently comprises both top‐down and bottom‐up methods. The top‐down techniques consist of physical alignment of DNA and lithographic patterning to organize DNA on surfaces. The bottom‐up approaches include lipid‐and polymer–DNA co‐assembly, the self‐assembly of DNA amphiphiles, and the remarkably specific and versatile methods of DNA nanotechnology. DNA‐based materials possess unprecedented molecular control and may offer innovative solutions in the fields of nanotechnology, sensing, nanomedicine, as well as optical and electronic devices. To realize the potential of these materials, a number of hurdles must be addressed. Bridging the gap between top‐down fabrication and bottom‐up assembly is of critical importance to the successful development of functional DNA‐based technology. A profound understanding of both regimes is necessary to achieve this goal. WIREs Nanomed Nanobiotechnol 2013, 5:266–285. doi: 10.1002/wnan.1218 This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Biology-Inspired Nanomaterials > Nucleic Acid-Based Structures Nanotechnology Approaches to Biology > Nanoscale Systems in Biology
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".