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Record W2319494918 · doi:10.1021/ac303387a

Plasmonic Nanorice Antenna on Triangle Nanoarray for Surface-Enhanced Raman Scattering Detection of Hepatitis B Virus DNA

2013· article· en· W2319494918 on OpenAlexaff
Ming Li, Scott K. Cushing, Hongyan Liang, Savan Suri, Dongling Ma, Nianqiang Wu

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersWest Virginia UniversityNational Science Foundation
KeywordsRaman scatteringPlasmonChemistryBiosensorRaman spectroscopySurface plasmon resonanceBiomoleculeDetection limitOptoelectronicsNanotechnologyOpticsMaterials scienceNanoparticlePhysics

Abstract

fetched live from OpenAlex

The sensitivity and the limit of detection of Raman sensors are limited by the extremely small scattering cross section of Raman labels. Silver nanorice antennae are coupled with a patterned gold triangle nanoarray chip to create spatially broadened plasmonic "hot spots", which enables a large density of Raman labels to experience strong local electromagnetic field. Finite difference time domain simulations have confirmed that the quasi-periodic structure increases the intensity and the area of the surface plasmon resonance (SPR), which enhances the surface-enhanced Raman scattering (SERS) signal significantly. The SERS signal of the nanorice/DNA/nanoarray chip is compared with that of the nanorice/DNA/film chip. The SERS signal is greatly enhanced when the Ag nanorices are coupled to the periodic Au nanoarray instead of the planar film chip. The resulting spatially broadened SPR field enables the SERS biosensor with a limit of detection of 50 aM toward hepatitis B virus DNA with the capability of discriminating a single-base mutant of DNA. This sensing platform can be extended to detect other chemical species and biomolecules such as proteins and small molecules.

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 score0.003
Threshold uncertainty score0.568

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)0.0010.000

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 head0.232
Teacher spread0.219 · 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

Citations153
Published2013
Admission routes1
Has abstractyes

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