{ "cells": [ { "cell_type": "markdown", "id": "af01a16e-b5aa-474f-abe7-d87d325d660a", "metadata": {}, "source": [ "# Incoherent Summing for patchy surfaces\n", "\n", "If you have patchy areas on a surface (> coherence length of neutron) you may want to average the reflectivity signals from those different areas, a process called incoherent summing. In contrast if the lateral inhomogeneity lengthscale is less than the coherence length you want to be laterally averaging the scattering length density profile.\n", "\n", "This example demonstrates the incoherent summing using `MixedReflectModel`. The steps are to first set up `Structure` for each of the patchy areas. Don't forget that you can re-use objects across different structures to enforce constraints/reduce parameterisation.\n", "\n", "The example I'll create is a simple polymer layer on top of a silicon wafer, but it's the same process different systems. Incoherent averaging is used for: patchy lipid bilayers, thickness gradients of films across a surface, etc. I sometimes use ellipsometric thickness mapping to guide the incoherent summing in an NR analysis. \n", "\n", "This is a good paper that demonstrates incoherent averaging:\n", "\n", "> [Gresham, Isaac J., et al. \"Geometrical confinement modulates the thermoresponse of a poly (N-isopropylacrylamide) brush.\" Macromolecules 54.5 (2021): 2541-2550.](https://pubs.acs.org/doi/epdf/10.1021/acs.macromol.0c02775)" ] }, { "cell_type": "code", "execution_count": 1, "id": "c18bb6b1-2511-468f-8176-d04ee3756058", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from refnx.analysis import Parameter\n", "from refnx.reflect import (\n", " ReflectModel,\n", " SLD,\n", " Slab,\n", " Structure,\n", " MixedReflectModel,\n", " LipidLeaflet,\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "id": "dbdf44af-4f51-4de2-9eae-627260d15b52", "metadata": {}, "outputs": [], "source": [ "# SLDs\n", "air = SLD(0.0)\n", "sio2 = SLD(3.47)\n", "polymer = SLD(1.0)\n", "si = SLD(2.07)" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c6a6550-8eb5-45f0-8250-96ca0f60d79e", "metadata": {}, "outputs": [], "source": [ "# sio2 slab is common over all areas\n", "sio2_layer = Slab(25, sio2, 3)\n", "\n", "# the si/sio2 roughness is common across all areas\n", "si_roughness = Parameter(3.0)" ] }, { "cell_type": "markdown", "id": "a4bb9746-fbae-48a9-9f54-de17a2950b2e", "metadata": {}, "source": [ "We're going to assume that the polymer coated area has two different thicknesses, one of which is 50% of the other. This kind of information can often be determined by ellipsometry. More complex thickness variations can be modelled with analytical thickness distributions, e.g. convex or concave domes. Note that the constraint is automatically propagated, i.e. if you change `polymer_thickness_0.value`, then this will be propagated to `polymer_thickness_1`." ] }, { "cell_type": "code", "execution_count": 4, "id": "1f92cd2a-023b-4133-a9c3-ba572d089d99", "metadata": {}, "outputs": [], "source": [ "polymer_thickness_0 = Parameter(200.0)\n", "# the thickness constraint is applied automatically\n", "polymer_thickness_1 = polymer_thickness_0 * 0.5\n", "\n", "# polymer_thickness_1 = Parameter(constraint=polymer_thickness_0 * 0.5) # an alternate way of enforcing the constraint\n", "\n", "polymer_l_0 = Slab(polymer_thickness_0, polymer, 4)\n", "polymer_l_1 = Slab(polymer_thickness_1, polymer, 4)" ] }, { "cell_type": "code", "execution_count": 5, "id": "9532d4dc-f631-48cf-aa64-0302fc2ad0e5", "metadata": {}, "outputs": [], "source": [ "structure_bare = air | sio2 | si(np.inf, si_roughness)\n", "structure0 = air | polymer_l_0 | sio2 | si(np.inf, si_roughness)\n", "structure1 = air | polymer_l_1 | sio2 | si(np.inf, si_roughness)" ] }, { "cell_type": "markdown", "id": "7ba5bd10-8ccc-4cac-9905-1ec8b08de62c", "metadata": {}, "source": [ "Once we have the structures that we wish to model we can set up the `MixedReflectModel` to incoherently sum the areas. `MixedReflectModel` is very similar to `ReflectModel` in the way it adds background, performs resolution smearing, etc." ] }, { "cell_type": "code", "execution_count": 6, "id": "7acd8241-3b90-4118-8740-f762cb335e32", "metadata": {}, "outputs": [], "source": [ "model = MixedReflectModel(\n", " [structure_bare, structure0, structure1], scales=(0.2, 0.5, 1.3), bkg=1e-8\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "id": "48f1803b-1b67-4637-9fa5-ce38652652dd", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q = np.geomspace(0.007, 0.3, 201)\n", "plt.plot(q, model(q))\n", "plt.yscale(\"log\");" ] }, { "cell_type": "markdown", "id": "c28e4f07-52b9-4ad1-a714-ed7f2bbfb7a2", "metadata": {}, "source": [ "If you were paying attention you'll see that the reflectivity below the critical edge is greater than 1. This is a consequence of \n", "`MixedReflectModel.scales` adding up to more than 1. Let's adjust the scales and replot, with the reflectivities from the different areas also displayed.\n", "Note that the scales are applied in the same order that the individual structures were supplied to `MixedReflectModel`." ] }, { "cell_type": "code", "execution_count": 8, "id": "8c58ba92-9eda-40d3-8ab0-d61d0b30919c", "metadata": {}, "outputs": [], "source": [ "# Each of the scales is a Parameter, and are collectively held in a `Parameters` object that can be indexed.\n", "model.scales[0].value = 0.2 # structure_bare\n", "model.scales[1].value = 0.5 # structure0\n", "model.scales[2].value = 0.3 # structure1" ] }, { "cell_type": "code", "execution_count": 9, "id": "700f1d74-64c0-4b3d-ac71-ae81e516c5ef", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(q, model(q), label=\"incoherent sum\")\n", "plt.plot(q, structure_bare.reflectivity(q), label=\"bare\")\n", "plt.plot(q, structure0.reflectivity(q), label=\"s0\")\n", "plt.plot(q, structure1.reflectivity(q), label=\"s1\")\n", "plt.yscale(\"log\")\n", "plt.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }