Single-molecule methods in structural DNA nanotechnology
Bibliographic record
Abstract
Single molecules can now be visualised with unprecedented precision. As the resolution of single-molecule experiments improves, so too does the breadth, quantity and quality of information that can be extracted using these methodologies. In the field of DNA nanotechnology, we use programmable interactions between nucleic acids to generate complex, multidimensional structures. We can use single-molecule techniques - ranging from electron and fluorescence microscopies to electrical and force spectroscopies - to report on the structure, morphology, robustness, sample heterogeneity and other properties of these DNA nanoconstructs. In this Tutorial Review, we will detail how complementarity between static and dynamic single-molecule techniques can provide a unified image of DNA nanoarchitectures. The single-molecule methods that we discuss provide unprecedented insight into chemical and structural behaviour, yielding not just an average outcome but reporting on the distribution of values, ultimately showing how bulk properties arise from the collective behaviour of individual structures. As the fields of both DNA nanotechnology and single-molecule characterisation intertwine, a feedback loop is generated between disciplines, providing new opportunities for the development and operation of DNA-based materials as sensors, delivery vehicles, machinery and structural scaffolds.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".