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Record W2775088158 · doi:10.5539/jmsr.v7n1p1

DNA Origami as a Tool to Design Asymmetric Gold Nanostructures

2017· article· en· W2775088158 on OpenAlexvenueno aff
George Amoako, Ming Zhou, Moses Jojo Eghan, Samuel Sonko Sackey

Bibliographic record

VenueJournal of Materials Science Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaState Key Laboratory of TribologyTsinghua University
KeywordsDNA origamiNanotechnologyColloidal goldMaterials scienceTemplateNanostructureCircular dichroismDNAPlasmonDNA nanotechnologyNanoparticleCrystallographyChemistryOptoelectronics

Abstract

fetched live from OpenAlex

DNA origami technology provides a versatile approach for the chemical assembly of gold nanostructures. In this study the bottom-up approach of self-assembly using DNA in the origami process has been successfully applied to arrange five AuNPs asymmetrically. The DNA origami templates were modified to have binding sites that were extended with sticky ends to facilitate the attachment of the AuNPs. With the help of thiol chemistry, the AuNPs which were covered with DNA complementary to the sticky ends introduced on the DNA origami surfaces, we were able to attach the nanoparticles to the designed sites. It was realized that there were slight differences in the designed distances and the determined ones which were accounted for potentially by the deposition of the structures on the grids for imaging. The structures were characterized with gel electrophoresis and TEM. This asymmetric arrangement has the potential of exhibiting plasmonic behavior and circular dichroism when light is incident on the structure.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.017
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.055
GPT teacher head0.416
Teacher spread0.360 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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