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Quasinormal mode approach to modelling light-emission and propagation in nanoplasmonics

2014· article· en· W2001526372 on OpenAlexaff

Bibliographic record

VenueNew Journal of Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStrong Light-Matter Interactions
Canadian institutionsUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsPropagatorFormalism (music)Quasinormal modeQuantumExpression (computer science)ResonatorField (mathematics)Dipole

Abstract

fetched live from OpenAlex

We describe a powerful and intuitive theoretical technique for modeling light–matter interactions in classical and quantum nanoplasmonics. Our approach uses a quasinormal mode (QNM) expansion of the photon Green function within a metal nanoresonator of arbitrary shape, together with a Dyson equation, to derive an expression for the spontaneous decay rate and far field propagator from dipole oscillators outside resonators. For a single QNM, at field positions outside the quasi-static coupling regime, we give a closed form solution for the Purcell factor and generalized effective mode volume. We augment this with an analytic expression for the divergent local density of optical states very near the metal surface, which allows us to derive a simple and highly accurate expression for the electric field outside the metal resonator at distances from a few nanometers to infinity. This intuitive formalism provides an enormous simplification over full numerical calculations and fixes several pending problems in QNM theory. Introduction and background . When small metallic particles with dimensions much less than a wavelength (MNP) are illuminated with a beam of light, typically from a laser, the electric field strength at certain locations just outside of the metal surface can be enhanced by many orders of magnitude in comparison to the strength of the electric field from the laser beam that excites the particle. Many fields of science and sensor engineering have taken advantage of this property to boost the effective coupling strength of light to molecules or artificial atoms located near the MNP. Main result(s) . The largest field enhancements occur when the exciting radiation resonantly excites collective plasmon oscillations of the free electrons in the MNP. The resonant frequencies of the plasmon modes and the degree of field enhancement depend critically on the shape of the MNP, but analytic solutions are only available for spheres. Accurate numerical models needed for non-spherical geometries are extremely computational-time-intensive. The main result of this paper is a prescription for dramatically reducing the time required to numerically model the electromagnetic response of arbitrarily shaped MNP, assuming that only one or a small number of plasmon modes are involved in the problem. The approach described in the manuscript also offers considerable insight into the underlying mechanisms, that are typically obscured in brute force numerical simulations. As an example application, we study the enhanced spontaneous emission rate of a dipole emitter over a wide range of spatial positions and frequencies, and show excellent agreement with full numerical solutions. Wider implications . There is a rapidly growing "industry" of nanoplasmonic engineering, for both scientific and technological applications. The results of this work should benefit that community by offering intuitive insights that will help in the design of MNP shape and size for a particular application, and also greatly reduce the time required to obtain accurate numerical simulations of the systems response. Our quasianalytical approach can be applied to a wide range of problems in classical and quantum nanoplasmonics.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 designSimulation or modeling
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

Citations108
Published2014
Admission routes1
Has abstractyes

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