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Record W3217088281 · doi:10.1039/d1an01421h

Core-satellite assembly of gold nanoshells on solid gold nanoparticles for a color coding plasmonic nanosensor

2021· article· en· W3217088281 on OpenAlexafffund
Nguyen H. Le, Nicole Cathcart, Vladimir Kitaev, Jennifer I. L. Chen

Bibliographic record

VenueThe Analyst · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsWilfrid Laurier UniversityYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsPlasmonNanoshellColloidal goldMaterials scienceNanotechnologyNanoparticleNanosensorPlasmonic nanoparticlesBiomoleculeOptoelectronics

Abstract

fetched live from OpenAlex

the layer-by-layer assembly of nanoparticles linked by DNA. We used finite-difference time-domain simulations to help guide the geometrical design, and characterized the optical properties and morphology of the solid-shell nanoparticle assemblies using darkfield microscopy, single-nanostructure spectroscopy, and scanning electron microscopy. Plasmon coupling yielded resonant peaks at longer wavelengths in the red to near-infrared range for solid-shell assemblies compared with solid-solid nanoparticle assemblies. We examined sensing with the solid-shell assemblies using adenosine triphosphate (ATP) as a model target and ATP-aptamer as the linker. Binding of ATP induced disassembly and led to a decrease in the scattering intensity and a color change from red to green. The new morphology of the core-satellite assembly enabled plasmonic color-coding of multiplexed sensors. We demonstrate this potential by fabricating two types of assemblies using DNA linkers that target different molecules - ATP and a model nucleic acid. Our work expands the capability of chip-based plasmonic nanoparticle assemblies for the analysis of multiple, different types of biomolecules in small sample sizes including the microenvironment and single cells.

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)0.001
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 score0.015
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.061
GPT teacher head0.302
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

Citations4
Published2021
Admission routes2
Has abstractyes

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