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Record W2068321080 · doi:10.1021/nl502746h

Structural and Optical Properties of Self-Assembled Chains of Plasmonic Nanocubes

2014· article· en· W2068321080 on OpenAlexafffund
Anna Klinkova, Héloïse Thérien‐Aubin, Aftab Ahmed, Dmytro Nykypanchuk, Rachelle M. Choueiri, Brandon Gagnon, Anastasiya Muntyanu, Oleg Gang, Gilbert C. Walker, Eugenia Kumacheva

Bibliographic record

VenueNano Letters · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsUniversity of Toronto
FundersBrookhaven National LaboratoryArgonne National LaboratoryBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaOntario Trillium FoundationUniversity of TorontoU.S. Department of Energy
KeywordsNanorodMaterials sciencePlasmonSelf-assemblyNanoparticlePolymerRaman scatteringRaman spectroscopyNanotechnologyChemical physicsOptoelectronicsOpticsChemistryComposite material

Abstract

fetched live from OpenAlex

Solution-based linear self-assembly of metal nanoparticles offers a powerful strategy for creating plasmonic polymers, which, so far, have been formed from spherical nanoparticles and cylindrical nanorods. Here we report linear solution-based self-assembly of metal nanocubes (NCs), examine the structural characteristics of the NC chains, and demonstrate their advanced optical characteristics. In comparison with chains of nanospheres with similar dimensions, composition, and surface chemistry, predominant face-to-face assembly of large NCs coated with short polymer ligands led to a larger volume of hot spots in the chains, a nearly uniform E-field enhancement in the gaps between colinear NCs, and a new coupling mode for NC chains due to the formation of a Fabry-Perot resonator structure formed by face-to-face bonded NCs. The NC chains exhibited stronger surface-enhanced Raman scattering in comparison with linear assemblies of nanospheres. The experimental results were in agreement with finite difference time domain simulations.

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 score0.005
Threshold uncertainty score0.222

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)0.0000.000

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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

Citations106
Published2014
Admission routes2
Has abstractyes

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