# Diffractive array of gold nanorods

This example considers a square array of 200 Au nanorods with a large pitch (550nm); this configuration can lead to a diffractive coupling effect that strongly modifies the resonance of individual rods, despite their wide separation.

using CoupledDipole
using StaticArrays
using DataFrames
using VegaLite

## materials
wavelengths = collect(400:2:1000.0)
media = Dict([("Au", epsilon_Au), ("medium", x -> 1.33)])
mat = Material(wavelengths, media)

## array geometry
# N, Λ, a, b, c, φ, θ, ψ, material = "Au", type="particle"
cl0 = cluster_single(80, 40, 40)
cl1 = cluster_array(200, 550, 80, 40, 40)
Cluster{Float64, Float64, Int64}(SVector{3, Float64}[[-3575.0, -3575.0, 0.0], [-3025.0, -3575.0, 0.0], [-2475.0, -3575.0, 0.0], [-1925.0, -3575.0, 0.0], [-1375.0, -3575.0, 0.0], [-825.0, -3575.0, 0.0], [-275.0, -3575.0, 0.0], [275.0, -3575.0, 0.0], [825.0, -3575.0, 0.0], [1375.0, -3575.0, 0.0]  …  [-1375.0, 3575.0, 0.0], [-825.0, 3575.0, 0.0], [-275.0, 3575.0, 0.0], [275.0, 3575.0, 0.0], [825.0, 3575.0, 0.0], [1375.0, 3575.0, 0.0], [1925.0, 3575.0, 0.0], [2475.0, 3575.0, 0.0], [3025.0, 3575.0, 0.0], [3575.0, 3575.0, 0.0]], Rotations.QuatRotation{Float64}[[1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]  …  [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0]], SVector{3, Int64}[[80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40]  …  [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40], [80, 40, 40]], ["Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au"  …  "Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au", "Au"], "particle")

We'll do the simulation at normal incidence, and simulate the properties of a single rod for reference,

## incidence: along z
Incidence = [SVector(0,0,0)]

disp1 = spectrum_dispersion(cl0, mat, Incidence)
disp2 = spectrum_dispersion(cl1, mat, Incidence)

d1 = dispersion_df(disp1, mat.wavelengths)
d2 = dispersion_df(disp2, mat.wavelengths)

d = [insertcols!(d1, :cluster => "single");
insertcols!(d2, :cluster => "array")]

@vlplot(data=d,
width= 400,
height =  300,
mark = {:line},
row = "crosstype",
resolve={scale={y="independent"}},
encoding = {x = "wavelength:q", y = "value:q", color = "variable:n", strokeDash="cluster:n"}
)

Note that scattering cross-sections are terribly inaccurate here; because of the large size of the cluster the numerical quadrature would require a large number of scattering angles. It would be preferable to evaluate scattering as the difference between extinction and absorption.