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Record W2027101112 · doi:10.1021/ja1017442

Templated Synthesis of DNA Nanotubes with Controlled, Predetermined Lengths

2010· article· en· W2027101112 on OpenAlexafffund
Pik Kwan Lo, Florian Altvater, Hanadi F. Sleiman

Bibliographic record

VenueJournal of the American Chemical Society · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsDNADNA origamiNanotechnologyNanotubeChemistryDNA nanotechnologyA-DNANanostructureCarbon nanotubeBiophysicsMaterials scienceBiology

Abstract

fetched live from OpenAlex

We report a DNA-templated approach to construct nanotubes with controlled lengths and narrow molecular weight distribution, allowing the deliberate variation of this length. This approach relies on the facile and modular assembly of a DNA guide strand of precise length that contains single-stranded gaps repeating at every 50 nm. This is followed by positioning triangular DNA "rungs" on each of these single-stranded gaps and adding identical linking strands to the two other sides of the triangles to close the DNA nanotubes. The length of the guide strand can be deliberately changed. We show the use of this approach to produce nanotubes with lengths of 1 microm or 500 nm and narrow length distributions. This is in contrast to nontemplated approaches, which lead to long and polydisperse nanotubes. We also demonstrate the encapsulation of 20 nm gold nanoparticles within these well-defined nanotubes to form finite lines of gold nanoparticles with longitudinal plasmon coupling, with a number of potential nanophotonic applications. This guiding strand approach is a useful tool in the creation of DNA nanostructures, in this case allowing the use of a simple template generated by a minimal number of DNA strands to program the length and molecular weight distribution of assemblies, as well as to organize any number of DNA-labeled nano-objects into finite structures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.240
Teacher spread0.236 · 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 teacher head, 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

Citations66
Published2010
Admission routes2
Has abstractyes

Explore more

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