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Record W4404544110 · doi:10.1002/smll.202407604

Silver(I)‐Mediated 2D DNA Nanostructures

2024· article· en· W4404544110 on OpenAlexfundno aff
Simon Vecchioni, R. Lo, Qiuyan Huang, Kun Wang, Yoel P. Ohayon, Ruojie Sha, Lynn J. Rothschild, Shalom J. Wind

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersOffice of Naval ResearchOffice of Naval Research GlobalYork UniversityNational Aeronautics and Space AdministrationU.S. Department of EnergyNational Science Foundation
KeywordsNanotechnologyNanostructureDNANanoscopic scaleMaterials scienceNucleic acidNanometreHelix (gastropod)DNA origamiChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Structural DNA nanotechnology enables the self‐organization of matter at the nanometer scale, but approaches to expand the inorganic and electrical functionality of these scaffolds remain limited. Developments in nucleic acid metallics have enabled the incorporation of site‐specific metal ions in DNA duplexes and provide a means of functionalizing the double helix with atomistic precision. Here a class of 2D DNA nanostructures that incorporate the cytosine‐Ag + ‐cytosine (dC:Ag + :dC) base pair as a chemical trigger for self‐assembly is described. It is demonstrated that Ag + ‐functionalized DNA can undergo programmable assembly into large arrays and rings, and can be further coassembled with guanine tetraplexes (G4). It is shown that 2D DNA lattices can be assembled with a variety of embedded nanowires at tunable spacing. These results serve as a foundation for further development of self‐assembled, metalated DNA nanostructures, with potential for high‐precision DNA nanoelectronics with nanometer pitch.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.250
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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