MétaCan
Menu
Back to cohort
Record W2326839602 · doi:10.1021/nn4006329

Simple Design for DNA Nanotubes from a Minimal Set of Unmodified Strands: Rapid, Room-Temperature Assembly and Readily Tunable Structure

2013· article· en· W2326839602 on OpenAlexaff
Graham D. Hamblin, Amani A. Hariri, Karina M. M. Carneiro, Kai Lin Lau, Gonzalo Cosa, Hanadi F. Sleiman

Bibliographic record

VenueACS Nano · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanotechnologyMaterials sciencePolymerizationDNANanotubeMonomerNanoscopic scaleDNA origamiNanowireAnnealing (glass)TileChemistryNanostructureCarbon nanotubePolymer

Abstract

fetched live from OpenAlex

DNA nanotubes have great potential as nanoscale scaffolds for the organization of materials and the templation of nanowires and as drug delivery vehicles. Current methods for making DNA nanotubes either rely on a tile-based step-growth polymerization mechanism or use a large number of component strands and long annealing times. Step-growth polymerization gives little control over length, is sensitive to stoichiometry, and is slow to generate long products. Here, we present a design strategy for DNA nanotubes that uses an alternative, more controlled growth mechanism, while using just five unmodified component strands and a long enzymatically produced backbone. These tubes form rapidly at room temperature and have numerous, orthogonal sites available for the programmable incorporation of arrays of cargo along their length. As a proof-of-concept, cyanine dyes were organized into two distinct patterns by inclusion into these DNA nanotubes.

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 score0.016
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.253
Teacher spread0.241 · 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

Citations50
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueACS NanoSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207