From Exhaustive Simulations to Key Principles in DNA Nanoelectronics
Bibliographic record
Abstract
Charge transfer can take place along double-helical DNA over distances as long as 30 nm. However, given the active role of the thermal environment surrounding charge carriers in DNA, physical mechanisms driving the transfer process are highly debated. Moreover, the overall potential of DNA to act as a conducting material in nanoelectronic circuits is questionable. Here, we identify key principles in DNA nanoelectronics by performing an exhaustive computational study. The electronic structure of double-stranded DNA is described with a coarse-grained model. The dynamics of the molecular system and its environment is taken into account using a quantum scattering method, mimicking incoherent, elastic, and inelastic effects. By analyzing all possible sequences with 3–7 base pairs, we identify fundamental principles in DNA nanoelectronics: The environment crucially influences the electrical conductance of DNA and the majority of sequences conduct via a mixed, coherent–incoherent mechanism. Likewise, the metal–molecule coupling and the gateway states play significant roles in the transport behavior. While most sequences analyzed here are exposed to be rather poor electrical conductors, we identify exceptional DNA molecules, which we predict to be excellent and robust conductors of electric current over a wide range of physical conditions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".