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Can transverse plasmonic fields be revealed by differential phase contrast?

2016· other· en· W4238772171 on OpenAlexaff
Stefan Löffler, Edson P. Bellido, Isobel C. Bicket, Gianluigi A. Botton

Bibliographic record

VenueEuropean Microscopy Congress 2016: Proceedings · 2016
Typeother
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPlasmonPhysicsSurface plasmonOpticsElectronElectric fieldTransverse planePerpendicularOptical axisCondensed matter physicsLens (geology)Quantum mechanics

Abstract

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Surface plasmons give rise to a wide range of applications from molecular sensors [1] over novel circuit designs [2] to the design of meta‐materials with highly unusual optical properties [3]. Of particular importance are localized surface plasmons (LSPs) that are confined to the surface of nanoparticles as they can give rise to a significant enhancement of electromagnetic fields in the vicinity of the nanostructure. Because LSPs typically are confined to the nanometer regime, TEM is ideally suited for mapping those charge oscillations. So far, the predominant method of studying plasmon oscillations has been EELS, which allows mapping the strength of selected resonance modes by measuring the energy loss probability of the probe beam for different LSP energies. In a non‐relativistic approximation, this energy loss is brought about by the component of the electric field along the optical axis (and, in principle, the magnetic component perpendicular to the optical axis) of the excited plasmon resonance. Thus, it is impossible to gain any information about the electric field in the viewing plane (i.e., perpendicular to the optical axis). Precisely this component can, however, be studied using differential phase contrast (DPC) [4,5]. DPC exploits the fact that electrons subject to an electromagnetic field are deflected according to the Lorentz force. Any deflection along the optical axis gives rise to a change in kinetic energy and, hence, shows up in EELS. Any deflection perpendicular to the optical axis, however, changes the direction of the electron's momentum, but not its magnitude (in first order approximation). This gives rise to a shift in the electron's momentum distribution. The final momentum distribution, after passing the nanostructure, can then conveniently be measured in the TEM's diffraction plane. Compared to a reference measured without field, the displacement of the transmitted beam shows a shift that is proportional to the field integrated along the electron trajectory. Here, we used the MNPBEM toolbox [6,7] to simulate the plasmonic response of a 200x50x50 nm³ Ag nanorod to the electron beam (see fig. 1). From the data of the surface charges and currents, we then calculated the EELS maps (see fig. 1) and in‐plane deflections along a line parallel to the nanorod (see fig. 2) for different plasmonic modes. The EELS maps show the typical excitation probabilities for the first two modes with two and three maxima. The in‐plane electric field components show a similar behavior in general, although the local extrema are less pronounced. The DPC deflections are found to be in good agreement with the electric field with some small differences close to the center of the rod which can be attributed to the cumulative nature of the DPC deflections as well as retardation effects. The absolute magnitude of the DPC deflections in fig. 2 is of the order of 0.1 µrad at 300 keV which, albeit small, should be measurable with latest generation TEMs when using large camera lengths and/or the LACBED technique. In addition, the deflections can be increased, e.g., by using a lower acceleration voltage. This work shows that it should be feasible to determine all three components of the electromagnetic field caused by plasmons using a combination of DPC and EELS using state‐of‐the‐art TEMs. This will open up new possibilities for understanding and designing novel plasmonic devices.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.259
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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