DNA-Based Metallosupramolecular Materials
Bibliographic record
Abstract
This chapter reviews the current methods for site-specific incorporation of transition metals into DNA, and the applications of the resulting metal–DNA nanostructures. DNA has recently emerged as a powerful template for the programmable positioning of molecules and materials on the nanometre scale. Under specific conditions, DNA has also been shown to mediate long-range charge transport. The introduction of metals into DNA can impart this molecule with important properties, such as increased stability, redox activity, photochemical, catalytic as well as magnetic properties. In turn, the use of DNA can result in the organization of transition metal complexes into any deliberately designed structure, be it symmetrical or aperiodic. This is currently difficult to achieve using conventional supramolecular chemistry. This programmed organization of transition metals could lead to the applications of these molecules in nanoelectronics, nanooptics, data storage, light harvesting and catalysis. This review describes the synthetic approaches that achieve site-specific incorporation of metals into DNA, and the resulting synergistic ability of metals and DNA to enhance each other's properties and applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".