Signal Propagation in Surface-Confined DNA Circuits with Rigidified DNA Origami
Bibliographic record
Abstract
Surface-confined DNA computing has emerged as a powerful information processing paradigm, offering enhanced specificity and accelerated reaction kinetics. Artificially designed DNA origami serves as a key enabler for such systems by providing a highly programmable platform for positioning computational components with nanometer resolution. However, conventional monolayer DNA origami circuits often exhibit non-negligible signal leakage, attributed to structural fluctuation-induced crosstalk between surface-confined molecules. Here, we utilize a rigidified double-layered uniaxial DNA origami platform to suppress the intrinsic structural fluctuation, thereby achieving high-fidelity signal propagation via minimizing fluctuation-mediated leakage. The rigidified DNA origami serves as a reliable computing platform to provide site arrangements that are narrowly distributed and closer to theoretical design. Basic propagation modules of varying orientations and spacings are implemented with reduced leakage and increased on-off ratios. We further demonstrate high-performance parallel transmission lines and logic gates on rigidified DNA origami. This approach establishes a generalizable strategy for engineering reliable platforms for surface-confined DNA computing.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".