Computational design of three Cu-induced triangular pyrimidines based DNA motifs with improved conductivity
Bibliographic record
Abstract
Novel DNA triangular pyrimidine derivatives are designed by metal decoration through replacement of H by Cu in the Watson–Crick hydrogen bond region. The DFT method is used to examine the coordination of triangle-arranged Cu with three pyrimidines in nonplanar three-bladed turbine geometries. The Cu···Cu cuprophilic bonds are ascribed to the partially occupied d orbitals without direct molecular orbital (MO) interactions. Four-center bonds depend on Cu–N/O bonds, which are contributed by p orbitals of N/O atoms along or perpendicular to the bond axis. The activity of frontier MOs is modulated, leading to the decrease of gaps, ionization potentials (IPs), and electron affinities (EAs) desired for the improvement of conductivity. The hole trapping ability is assured by virtue of the spin density distributed on Cu. On average, the single electron density is located on π orbitals of three aromatic base rings. There is paramagnetic electron delocalization on the inner d orbitals of triangle region. The analysis of electron localization function ELF-π and electrostatic potential maps reveals that the outer strong π–π stacking interaction together with the inner d orbital channel enable effective transduction of electrical signals along the Cu–DNA nanowires. The 3Cu-induced triangular pyrimidines have important potential applications as structural motifs of molecular electronic devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".