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Record W2019438854 · doi:10.1039/c3cp52771a

Selective dehybridization of DNA–Au nanoconjugates using laser irradiation

2013· article· en· W2019438854 on OpenAlexaff
Hidehiko Asanuma, Zhifeng Jiang, Katsuyoshi Ikeda, Kohei Uosaki, Hua‐Zhong Yu

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDNANanotechnologyColloidal goldOligonucleotidePlasmonMaterials scienceNanoparticleLaserChemistryOptoelectronics

Abstract

fetched live from OpenAlex

Plasmonic heating to trigger release of oligonucleotides from nanoconjugates is potentially useful for therapeutic purposes and designed assembly of DNA nanostructures. In the past, great controllability has been achieved by introducing distinctive absorption nanoparticle centers, where the anchoring bond (e.g., sulfur-gold bond) has been selectively broken. Instead of releasing the surface-bound duplex DNA via breakage of the gold-sulphur anchor bond, selective and non-destructive dehybridization of DNA under a "mild" condition on different gold nanoconjugates is demonstrated in this work. This finding will permit sequential dehybridization/release of DNA at specific regions of a complex system; thus it can be extended to control gene expression and to manipulate an assembly of highly organized DNA constructs. Particularly we show herein the feasibility of selectively dehybridizing DNA-Au nanoconjugates via localized plasmonic heating, which is accomplished by controlling the laser wavelength, power, and irradiation time.

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 score0.003

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)0.0010.000

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 head0.231
Teacher spread0.218 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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