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Record W2292062080 · doi:10.1021/jacs.6b00751

Engineering the Structure and Properties of DNA-Nanoparticle Superstructures Using Polyvalent Counterions

2016· article· en· W2292062080 on OpenAlexafffund
Leo Y. T. Chou, Fayi Song, Warren C. W. Chan

Bibliographic record

VenueJournal of the American Chemical Society · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchProstate Cancer Canada
KeywordsCounterionDNA condensationNanoparticleChemistryPolyelectrolyteDNANanostructureNanotechnologyPolymerTemplateMaterials scienceIonOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

DNA assembly of nanoparticles is a powerful approach to control their properties and prototype new materials. However, the structure and properties of DNA-assembled nanoparticles are labile and sensitive to interactions with counterions, which vary with processing and application environment. Here we show that substituting polyamines in place of elemental counterions significantly enhanced the structural rigidity and plasmonic properties of DNA-assembled metal nanoparticles. These effects arose from the ability of polyamines to condense DNA and cross-link DNA-coated nanoparticles. We further used polyamine wrapped DNA nanostructures as structural templates to seed the growth of polymer multilayers via layer-by-layer assembly, and controlled the degree of DNA condensation, plasmon coupling efficiency, and material responsiveness to environmental stimuli by varying polyelectrolyte composition. These results highlight counterion engineering as a versatile strategy to tailor the properties of DNA-nanoparticle assemblies for various applications, and should be applicable to other classes of DNA nanostructures.

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 score0.002
Threshold uncertainty score0.169

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 head0.007
GPT teacher head0.232
Teacher spread0.224 · 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

Citations56
Published2016
Admission routes2
Has abstractyes

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