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Record W2899334414 · doi:10.1021/acsanm.8b01964

Selective Plasmonic Sensing and Highly Ordered Metallodielectrics via Encapsulation of Plasmonic Metal Nanoparticles with Metal Oxides

2018· article· en· W2899334414 on OpenAlexafffund
Nicole Cathcart, Nimer Murshid, P. Campbell, Vladimir Kitaev

Bibliographic record

VenueACS Applied Nano Materials · 2018
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research FundCanada Foundation for Innovation
KeywordsMaterials scienceSurface plasmon resonancePlasmonNanoparticleOxideNanotechnologyMetalPlasmonic nanoparticlesColloidal goldChemical engineeringOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

Novel materials for sensing and metallodielectric arrays have been prepared by encapsulation of gold-protected shape-selected silver nanoparticles with a diverse range of shells including iron, manganese, and iridium oxides using a versatile deposition procedure. These shells of varying thickness, porosity, and smoothness encapsulating metal nanoparticles advantageously combine functionalities of plasmonic cores and oxide shells into resulting functional materials. In particular, the size-uniform metal oxide encapsulated metal nanoparticles (MO-MNPs) assemble into well-ordered metallodielectric arrays for plasmonic applications. The shape and the localized surface plasmon resonance (LSPR) of the metal cores are well preserved, while the chemical and colloidal stability are enhanced by the formation of oxide shells. Metal oxide shells impart the selectivity of detection in surface-enhanced Raman spectroscopy (SERS) and surface plasmon resonance (SPR) sensing. Notably, selective sub-millimolar SPR detection of phosphate ions has been demonstrated.

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 score0.004
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.212
Teacher spread0.201 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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