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Record W2482026876 · doi:10.1021/acsnano.6b03796

Electron Energy Loss Spectroscopy Investigation into Symmetry in Gold Trimer and Tetramer Plasmonic Nanoparticle Structures

2016· article· en· W2482026876 on OpenAlexafffund
Steven J. Barrow, Sean M. Collins, David Rossouw, Alison M. Funston, Gianluigi A. Botton, Paul A. Midgley, Paul Mulvaney

Bibliographic record

VenueACS Nano · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsMcMaster University
FundersSeventh Framework ProgrammeAustralian Research CouncilEuropean Research CouncilBill and Melinda Gates FoundationEuropean CommissionNatural Sciences and Engineering Research Council of CanadaUniversity of MelbourneRoyal Society
KeywordsTrimerTetramerPlasmonElectron energy loss spectroscopyPlasmonic nanoparticlesMolecular physicsScanning transmission electron microscopySpectroscopyNanoparticleMaterials scienceChemical physicsSymmetry (geometry)NanotechnologyTransmission electron microscopyChemistryOptoelectronicsPhysicsDimerGeometry

Abstract

fetched live from OpenAlex

We present a combined scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) investigation into the mode symmetries of plasmonic nanoparticle trimer and tetramer structures. We obtain nanometer-resolved energy loss spectra for both trimer and tetramer structures and compare these to boundary element method simulations. We show that EELS, in conjunction with eigenmode simulations, offers a complete characterization of the individual superstructures, and we trace the evolution of both optically dark and bright modes and identify multipolar mode contributions. We then apply this technique to tetramer structures that exhibit an expanded range of mode symmetries for two-dimensional and three-dimensional self-assembled geometries. These findings provide a comprehensive experimental account of the available photonic states in self-assembled nanoparticle clusters.

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 score0.371

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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

Citations55
Published2016
Admission routes2
Has abstractyes

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