### Size-distribution model: size-spacing coupling approximation

Scattering from cylinders of two different sizes using the Size-Spacing Coupling Approximation.

• The sample is made of cylinders deposited on a substrate.
• The distribution of particles is made of:
• 80% of cylinders with radii and heights equal to $5$ nm
• 20% of cylinders with radii and heights equal to $8$ nm.
• The interference function is Radial Paracrystal with a peak distance of $18$ nm and a damping length of $1$ $\mu$m.
• The wavelength is equal to 0.1 nm.
• The incident angles are $\alpha_i = 0.2 ^{\circ}$ and $\varphi_i = 0^{\circ}$.
• The Size-Spacing Coupling Approximation is implemented using the function setApproximation. By default the Decoupling Approximation is used (see Size-distribution model: Decoupling Approximation).
• For this size-distribution model, an additional dimensionless parameter, the coupling parameter Kappa, has to be specified (see line 33). It defines how the distance between particles is linked with their sizes.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65  #!/usr/bin/env python3 """ Cylinders of two different sizes in Size-Spacing Coupling Approximation """ import bornagain as ba from bornagain import ba_plot as bp, deg, nm def get_sample(): """ A sample with cylinders of two different sizes on a substrate. The cylinder positions are modelled in Size-Spacing Coupling Approximation. """ # Materials material_particle = ba.RefractiveMaterial("Particle", 0.0006, 2e-08) material_substrate = ba.RefractiveMaterial("Substrate", 6e-06, 2e-08) vacuum = ba.RefractiveMaterial("Vacuum", 0, 0) # Form factors ff_1 = ba.Cylinder(5*nm, 5*nm) ff_2 = ba.Cylinder(8*nm, 8*nm) # Particles particle_1 = ba.Particle(material_particle, ff_1) particle_2 = ba.Particle(material_particle, ff_2) # Interference functions iff = ba.InterferenceRadialParacrystal(18*nm, 1000*nm) iff.setKappa(1) iff_pdf = ba.Profile1DGauss(3*nm) iff.setProbabilityDistribution(iff_pdf) # Particle layouts layout = ba.ParticleLayout() layout.addParticle(particle_1, 0.8) layout.addParticle(particle_2, 0.2) layout.setInterference(iff) layout.setTotalParticleSurfaceDensity(0.01) # Layers layer_1 = ba.Layer(vacuum) layer_1.addLayout(layout) layer_2 = ba.Layer(material_substrate) # Sample sample = ba.MultiLayer() sample.addLayer(layer_1) sample.addLayer(layer_2) return sample def get_simulation(sample): beam = ba.Beam(1e9, 0.1*nm, 0.2*deg) detector = ba.SphericalDetector(200, 2*deg, 1*deg, 1*deg) simulation = ba.ScatteringSimulation(beam, sample, detector) return simulation if __name__ == '__main__': sample = get_sample() simulation = get_simulation(sample) result = simulation.simulate() bp.plot_simulation_result(result) 
auto/Examples/scatter2d/ApproximationSSCA.py