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Eigenmode Tomography of Surface Charge Oscillations of Plasmonic Nanoparticles by Electron Energy Loss Spectroscopy


Type

Article

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Authors

Collins, SM 
Duchamp, M 
Saghi, Z 
Dunin-Borkowski, RE 

Abstract

Plasmonic devices designed in three dimensions enable careful tuning of optical responses for control of complex electromagnetic interactions on the nanoscale. Probing the fundamental characteristics of the constituent nanoparticle building blocks is, however, often constrained by diffraction-limited spatial resolution in optical spectroscopy. Electron microscopy techniques, including electron energy loss spectroscopy (EELS), have recently been developed to image surface plasmon resonances qualitatively at the nanoscale in three dimensions using tomographic reconstruction techniques. Here, we present an experimental realization of a distinct method that uses direct analysis of modal surface charge distributions to reconstruct quantitatively the three-dimensional eigenmodes of a silver right bipyramid on a metal oxide substrate. This eigenmode tomography removes ambiguity in two-dimensional imaging of spatially-localized plasmonic resonances, reveals substrate-induced mode degeneracy breaking in the bipyramid, and enables EELS for the analysis not of a particular electron-induced response but of the underlying geometric modes characteristic of particle surface plasmons.

Description

Keywords

surface plasmon, EELS, tomography, nanoparticles

Journal Title

ACS Photonics

Conference Name

Journal ISSN

2330-4022
2330-4022

Volume Title

2

Publisher

American Chemical Society (ACS)
Sponsorship
European Research Council (291522)
European Commission (312483)
S.M.C. acknowledges support of a Gates Cambridge Scholarship. E.R. acknowledges support from the Royal Society's Newton International Fellowship scheme and a Trinity Hall Research Fellowship. We thank Ben Knappet for assistance with the synthesis of the silver bipyramids. We thank F.J. de la Peña for helpful discussions on the use of HYPERSPY. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Program (No. FP7/2007-2013)/ERC Grant Agreement No. 291522-3DIMAGE and the European Union's Seventh Framework Program under a contract for an Integrated Infrastructure Initiative (Reference No. 312483-ESTEEM2)