{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", " **Chapter 4: [Spectroscopy](CH4_00-Spectroscopy.ipynb)** \n", "\n", "
\n", "\n", "\n", "\n", "# Introduction to Core-Loss Spectroscopy\n", "Working with X-Sections\n", "\n", "[Download](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_07-Introduction_Core_Loss.ipynb)\n", " \n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", " https://colab.research.google.com/github/gduscher/MSE672-Introduction-to-TEM/blob/main/Spectroscopy/CH4_07-Introduction_Core_Loss.ipynb)\n", "\n", "\n", "part of \n", "\n", " **[MSE672: Introduction to Transmission Electron Microscopy](../_MSE672_Intro_TEM.ipynb)**\n", "\n", "**Spring 2024**\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Gerd Duscher Khalid Hattar
Microscopy Facilities Tennessee Ion Beam Materials Laboratory
Materials Science & Engineering Nuclear Engineering
Institute of Advanced Materials & Manufacturing
The University of Tennessee, Knoxville
\n", "Background and methods to analysis and quantification of data acquired with transmission electron microscopes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Core --Loss Spectroscopy\n", "\n", "As we can see in figure below the energies of the core shells are well defined and can be viewed as delta functions, unlike in the low loss region where the broad valence bands are the initial state. In both cases, however, we excite electrons into the conduction band.\n", "\n", "\"core-loss\"\n", "*Excitation from a core-shell state up into the conduction band above the Fermi level.*\n", "\n", "If we look at the transition between two states $< \\Phi_f | H | \\Phi_i >$ the transition should be quite sharp. In the case of the low-loss spectrum, we have many initial (the valence) states and many final (the conduction) states. The spectrum will be a convolution of these states. \n", "\n", "The features of the core--loss edges are, therefore, much sharper than any details in the low--loss region. Because only the final states contribute to the features. These sharp features enable a wide variety of analysis to determine the chemical compositions and chemical bonding, probing the local conduction band of the sample.\n", "\n", "\n", "\n", "### Chemical Composition\n", "\n", "In this chapter we use the area under the ionization edge to determine the chemical composition of a (small) sample volume. \n", "The equation used to determine the number of atoms per unit volume $N$ (also called areal density) is:\n", "\\begin{equation}\n", "I_{edge}(\\beta, \\Delta E) = N I_{0}(\\beta) \\sigma_{edge}(\\beta, \\Delta E)\n", "\\end{equation}\n", "\n", "$I_0$ is the number of electrons hitting the sample, and so directly comparable to the beam current.\n", "\n", "The equation can be approximated assuming that the spectrum has not been corrected for single scattering:\n", "\\begin{equation} \n", "I_{edge}(\\beta, \\Delta E) = N I_{low-loss}(\\beta,\\Delta E) \\sigma_{edge}(\\beta, \\Delta E)\n", "\\end{equation}\n", "where $\\beta$ is the collection angle and $\\sigma_{edge}$ is the **partial** cross--section (for energy window $\\Delta E$) for the core--loss excitation.\n", "\n", "\n", "> \n", "> It is this cross-section $ \\sigma_{edge}$ that we want to explorein this notebook.\n", ">\n", "We will do the chemical composition in the [next notebook](CH4_08-Chemical_Composition.ipynb)\n", "\n", "\n", "\n", "## Load important packages\n", "\n", "### Check Installed Packages\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import sys\n", "import importlib.metadata\n", "def test_package(package_name):\n", " \"\"\"Test if package exists and returns version or -1\"\"\"\n", " try:\n", " version = importlib.metadata.version(package_name)\n", " except importlib.metadata.PackageNotFoundError:\n", " version = '-1'\n", " return version\n", "\n", "if test_package('pyTEMlib') < '0.2024.2.3':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install --upgrade pyTEMlib -q\n", "\n", "print('done')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import all relevant libraries\n", "\n", "Please note that the EELS_tools package from pyTEMlib is essential." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You don't have igor2 installed. If you wish to open igor files, you will need to install it (pip install igor2) before attempting.\n", "You don't have gwyfile installed. If you wish to open .gwy files, you will need to install it (pip install gwyfile) before attempting.\n", "Symmetry functions of spglib enabled\n", "pyTEM version: 0.2024.02.2\n" ] } ], "source": [ "import sys\n", "%matplotlib ipympl\n", "if 'google.colab' in sys.modules: \n", " from google.colab import output\n", " from google.colab import drive\n", " output.enable_custom_widget_manager()\n", " \n", "import matplotlib.pylab as plt\n", "import numpy as np\n", "\n", "## We need to import a few important additional function from matplotlib, \n", "## because we want to demonstrate a few more hidden functionalities of the EELS_tools of pytTEMlib.\n", "from matplotlib.widgets import Cursor\n", "from matplotlib.patches import Rectangle\n", "from matplotlib.widgets import SpanSelector\n", "\n", "## import the configuration files of pyTEMlib (we need access to the data folder)\n", "import pyTEMlib\n", "import pyTEMlib.eels_tools as eels\n", "import pyTEMlib.eels_dialog_utilities as ieels\n", "\n", "# For archiving reasons it is a good idea to print the version numbers out at this point\n", "print('pyTEM version: ',pyTEMlib.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Cross-Sections\n", "\n", "\n", "The form factors are from:\n", "X-Ray Form Factor, Attenuation, and Scattering Tables\n", "NIST Standard Reference Database 66\n", "\n", " DOI: https://dx.doi.org/10.18434/T4HS32\n", "\n", "Detailed Tabulation of Atomic Form Factors, Photoelectric Absorption and Scattering Cross Section, and Mass Attenuation Coefficients for Z = 1-92 from E = 1-10 eV to E = 0.4-1.0 MeV\n", "C.T. Chantler,1 K. Olsen, R.A. Dragoset, J. Chang, A.R. Kishore, S.A. Kotochigova, and D.S. Zucker\n", "NIST, Physical Measurement Laboratory\n", "\n", "The cross sections are part of the pyTEMlib package and are stored as a pickled dictionary in the package data directory.\n", "\n", "Below are the lines for accessing the cross sections with eels_tools of pyTEMlib." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "Xsections = eels.get_x_sections()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Cross Sections\n", "\n", "Please add your favourite element ot the list of atomic numbers.\n", "\n", "With the code cell above we made the whole database of cross secitons available for this notebook." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f718bf9724874359a468fadf005ac877", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -----Input ------------ #\n", "atomic_numbers = [58, 28]\n", "# ----------------------- #\n", "fig, ax = plt.subplots()\n", "for Z in atomic_numbers:\n", " ax.plot(Xsections[str(Z)]['ene'], Xsections[str(Z)]['dat'], label = Xsections[str(Z)]['name'])\n", "\n", "ax.set_xlim(0,1500)\n", "ax.set_ylim(0,2.5e17)\n", "ax.set_xlabel('energy_loss (eV)')\n", "ax.set_ylabel('cross section (atoms/nm$^2$)')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List All Edges of an Element " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ce-O3: 19.8 eV \n", "Ce-O2: 19.8 eV \n", "Ce-O1: 37.8 eV \n", "Ce-N6: 85.9 eV \n", "Ce-N5: 110.0 eV \n", "Ce-N4: 110.0 eV \n", "Ce-N3: 207.2 eV \n", "Ce-N2: 223.3 eV \n", "Ce-N1: 289.6 eV \n", "Ce-M5: 883.3 eV \n", "Ce-M4: 901.3 eV \n", "Ce-M3: 1185.4 eV \n", "Ce-M2: 1272.8 eV \n", "Ce-M1: 1434.6 eV \n", "Ce-L3: 5723.4 eV \n", "Ce-L2: 6164.2 eV \n", "Ce-L1: 6548.8 eV \n", "Ce-K1: 40443.0 eV \n" ] } ], "source": [ "element = str(58)\n", "for key in Xsections[element]:\n", " if isinstance(Xsections[element][key], dict):\n", " if 'onset' in Xsections[element][key]:\n", " print(f\"{Xsections[element]['name']}-{key}: {Xsections[element][key]['onset']:8.1f} eV \")\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or ordered" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All edges\n", " Ni-K1: 8332.8 eV \n", " Ni-L1: 1008.1 eV \n", " Ni-L2: 871.9 eV \n", " Ni-L3: 854.7 eV \n", " Ni-M1: 111.8 eV \n", " Ni-M2: 68.1 eV \n", " Ni-M3: 68.1 eV \n", " Ni-M4: 3.6 eV \n", " Ni-M5: 3.6 eV \n", "Major edges\n", " Ni-K1: 8332.8 eV \n", " Ni-L3: 854.7 eV \n", " Ni-M5: 3.6 eV \n" ] } ], "source": [ "major_edges = ['K1', 'L3', 'M5', 'N5']\n", "all_edges = ['K1','L1','L2','L3','M1','M2','M3','M4','M5','N1', 'N2','N3','N4','N5','N6','N7','O1','O2','O3','O4','O5','O6','O7', 'P1', 'P2', 'P3']\n", "first_close_edges = ['K1', 'L3', 'M5', 'M3', 'N5', 'N3']\n", "\n", "element = str(28)\n", "\n", "def list_all_edges(Z):\n", " element = str(Z)\n", " print('All edges')\n", " for key in all_edges:\n", " if key in Xsections[element]:\n", " if 'onset' in Xsections[element][key]:\n", " print(f\" {Xsections[element]['name']}-{key}: {Xsections[element][key]['onset']:8.1f} eV \")\n", "\n", "def list_major_edges(Z):\n", " element = str(Z)\n", " print('Major edges')\n", " for key in major_edges:\n", " if key in Xsections[element]:\n", " if 'onset' in Xsections[element][key]:\n", " print(f\" {Xsections[element]['name']}-{key}: {Xsections[element][key]['onset']:8.1f} eV \") \n", "## Here with the function of the EELS_tools package \n", "list_all_edges(element)\n", "list_major_edges(element)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting all edges of an element in view\n", "\n", "Now, let's do it graphically" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f2496329c65c4f5fac7a5083b23fd994", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "major_edges = ['K1', 'L3', 'M5', 'N5']\n", "all_edges = ['K1','L1','L2','L3','M1','M2','M3','M4','M5','N1', 'N2','N3','N4','N5','N6','N7','O1','O2','O3','O4','O5','O6','O7', 'P1', 'P2', 'P3']\n", "first_close_edges = ['K1', 'L3', 'M5', 'M3', 'N5', 'N3']\n", "\n", "def get_Z(Z):\n", " \"\"\"\n", " returns the atomic number independent of input as a string or number\n", " \n", " input:\n", " Z: atomic number of chemical symbol (0 if not valid)\n", " \"\"\"\n", " Xsections = eels.get_x_sections()\n", " \n", " Z_out = 0\n", " if str(Z).isdigit(): \n", " Z_out = Z\n", " elif isinstance(Z, str):\n", " for key in Xsections:\n", " if Xsections[key]['name'].lower() == Z.lower(): ## Well one really should know how to write elemental \n", " Z_out = int(key)\n", " return Z_out\n", "\n", "\n", "class ElementalEdges(object):\n", " def __init__(self, ax, Z):\n", " self.ax = ax\n", " self.labels = None\n", " self.lines = None\n", " \n", " self.Z = get_Z(Z)\n", " self.color = 'black'\n", " self.Xsections = eels.get_x_sections()\n", " self.cid = ax.figure.canvas.mpl_connect('draw_event', self.onresize)\n", " \n", " #self.update()\n", " def set_edge(self,Z):\n", " self.Z = get_Z(Z)\n", " \n", " \n", " self.update()\n", " def onresize(self, event):\n", " self.update()\n", " \n", " def update(self):\n", " \n", " if self.labels != None:\n", " for label in self.labels:\n", " label.remove()\n", " if self.lines != None:\n", " for line in self.lines:\n", " line.remove()\n", " if self.Z>0:\n", " self.labels = [] ; self.lines =[] \n", " x_min, x_max = self.ax.get_xlim()\n", " y_min, y_max = self.ax.get_ylim()\n", " x_bounds = ax.get_xlim()\n", " element = str(self.Z)\n", " Xsections = self.Xsections\n", " for key in all_edges:\n", " if key in Xsections[element]:\n", " if 'onset' in Xsections[element][key]:\n", " x = Xsections[element][key]['onset']\n", " if x > x_min and x < x_max:\n", " if key in first_close_edges:\n", " label2 = self.ax.text(x, y_max,f\"{Xsections[element]['name']}-{key}\",\n", " verticalalignment='top', rotation = 0, color = self.color)\n", " else:\n", " label2 = self.ax.text(x, y_max,f\"\\n{Xsections[element]['name']}-{key}\",\n", " verticalalignment='top', color = self.color)\n", " line2 = self.ax.axvline(x,ymin = 0,ymax = 1,color=self.color)\n", "\n", " self.labels.append(label2)\n", "\n", " self.lines.append(line2)\n", " \n", " \n", " def disconnect(self):\n", " if self.labels != None:\n", " for label in self.labels:\n", " label.remove()\n", " if self.lines != None:\n", " for line in self.lines:\n", " line.remove()\n", " self.labels = None\n", " self.lines = None\n", " self.ax.figure.canvas.mpl_disconnect(self.cid)\n", " def reconnect(self): \n", " self.cid = ax.figure.canvas.mpl_connect('draw_event', self.onresize)\n", " ax.figure.canvas.draw_idle()\n", " \n", "fig, ax_Xsec = plt.subplots() \n", "for Z in atomic_numbers:\n", " ax_Xsec.plot(Xsections[str(Z)]['ene'], Xsections[str(Z)]['dat'], label = Xsections[str(Z)]['name'])\n", "ax_Xsec.set_xlim(100,1450)\n", "ax_Xsec.set_ylim(0,1e17)\n", "plt.legend(); \n", "Z = 58\n", "edges = ElementalEdges(ax_Xsec, 'Ce')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make the lines disappear" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "edges.disconnect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and reappear in the plot above" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "edges.set_edge(Z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's set another edge" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "edges.set_edge(28)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find Edges Listed in Xsection Dictionary\n", "\n", "please note that the two functions below are as ususal available in the EELS_tools of pyTEMlib" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Major Edges within 7.0 eV of 284.0\n", "\n", " C -K1: 283.8 eV \n", " Ru-M5: 279.4 eV \n", "\n", "All Edges within 7.0 eV of 284.0\n", "\n", " C -K1: 283.8 eV \n", " Kr-M1: 288.3 eV \n", " Sr-M2: 279.8 eV \n", " Ru-M5: 279.4 eV \n", " Ru-M4: 283.6 eV \n", " Ce-N1: 289.6 eV \n", " Eu-N2: 283.9 eV \n", " Gd-N2: 288.5 eV \n", " Tb-N3: 285.0 eV \n", " Os-N4: 289.4 eV \n" ] } ], "source": [ "# --- Input ----\n", "edge_onset = 284\n", "maximal_chemical_shift = 7\n", "# -------------\n", "print(f'Major Edges within {maximal_chemical_shift:.1f} eV of {edge_onset:.1f}')\n", "print(eels.find_all_edges(edge_onset, maximal_chemical_shift, major_edges_only=True))\n", "print(f'\\nAll Edges within {maximal_chemical_shift:.1f} eV of {edge_onset:.1f}')\n", "print(eels.find_all_edges(edge_onset, maximal_chemical_shift))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find Edges Depending on Cursor Postion" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "adcf249832f64b62b5a297620fdc9501", "version_major": 2, "version_minor": 0 }, "image/png": "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", "text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We are using \n", "# from matplotlib.widgets import Cursor\n", "\n", "maximal_chemical_shift = 5\n", "fig, ax = plt.subplots()\n", "plt.title(f'Click with left for major and right mouse button for all \\n ionization edges within {maximal_chemical_shift:.1f} eV of cursor')\n", "maximal_chemical_shift = 5\n", "cursor = ieels.EdgesAtCursor(ax, Xsections['42']['ene'], Xsections['42']['dat'],maximal_chemical_shift)\n", "cursor.maximal_chemical_shift =maximal_chemical_shift\n", "cid = plt.connect('motion_notify_event', cursor.mouse_move)\n", "\n", "ax.plot(Xsections['16']['ene'], Xsections['16']['dat']*2, label = 'S')\n", "ax.plot(Xsections['42']['ene'], Xsections['42']['dat'], 'r', label = 'Mo')\n", "ax.set_xlim(0,500)\n", "ax.set_ylim(0,2.5e17);\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## EELS cross sections\n", "### Determine Effective Collection Angle\n", "\n", "EELS cross sections are dependent on the momentum transfer (angle dependence), while photons cannot transfer any momentum. The angle dependence is given by the experimental set-up and can be calculated by the convolution of collection and convergence angle.\n", "\n", "Here we use the method of [Pierre Trebbia, Ultramicroscopy **24** (1988) pp.399-408](https://doi.org/10.1016/0304-3991(88)90130-1)\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "def effective_collection_angle(ene, alpha, beta, beam_kv):\n", " \"\"\" effective collection angle for convergent beam setup\n", " \n", " \n", " \n", " Original abstract of function y = effbeta(ene, alpha, beta, beamkV)\n", " # \n", " # This program computes etha(alpha,beta), that is the collection\n", " # efficiency associated to the following geometry :\n", " #\n", " # alpha = half angle of illumination (0 -> pi/2)\n", " # beta = half angle of collection (0 -> pi/2)\n", " # (pi/2 = 1570.795 mrad)\n", " #\n", " # A constant angular distribution of incident electrons is assumed\n", " # for any incident angle (-alpha,alpha). These electrons impige the\n", " # target and a single energy loss event occurs, with a characteristic\n", " # angle theta-e (relativistic). The angular distribution of the\n", " # electrons after the target is analytically derived.\n", " # This program integrates this distribution from theta=0 up to\n", " # theta=beta with an adjustable angular step.\n", " # This program also computes beta* which is the theoretical\n", " # collection angle which would give the same value of etha(alpha,beta)\n", " # with a parallel incident beam.\n", " #\n", " # subroutines and function subprograms required\n", " # ---------------------------------------------\n", " # none\n", " #\n", " # comments\n", " # --------\n", " #\n", " # The following parameters are asked as input :\n", " # accelerating voltage (kV), energy loss range (eV) for the study,\n", " # energy loss step (eV) in this range, alpha (mrad), beta (mrad).\n", " # The program returns for each energy loss step :\n", " # alpha (mrad), beta (mrad), theta-e (relativistic) (mrad),\n", " # energy loss (eV), etha (#), beta * (mrad)\n", " #\n", " # author :\n", " # --------\n", " # Pierre TREBBIA\n", " # US 41 : \"Microscopie Electronique Analytique Quantitative\"\n", " # Laboratoire de Physique des Solides, Bat. 510\n", " # Universite Paris-Sud, F91405 ORSAY Cedex\n", " # Phone : (33-1) 69 41 53 68\n", " #\n", " # \n", " \"\"\"\n", " \n", " \n", " z1 = beam_kv*1000. ; # eV\n", " z2 = ene[0];\n", " z3 = ene[-1]\n", " z4 = 100.0\n", " z5 = alpha*0.001 # rad\n", " z6 = beta*0.001 # rad\n", " z7 = 500 # number of integration steps to be modified at will\n", "\n", " # main loop on energy loss\n", " \n", " for zx in range(int(z2),int(z3),int(z4)): #! zx = current energy loss\n", " eta=0.0;\n", " x0=float(zx)*(z1+511060.)/(z1*(z1+1022120.)); # x0 = relativistic theta-e\n", " x1 = np.pi/(2.*x0);\n", " x2=x0*x0+z5*z5;\n", " x3=z5/x0*z5/x0;\n", " x4=0.1*np.sqrt(x2);\n", " dtheta=(z6-x4)/z7;\n", " #\n", " # calculation of the analytical expression\n", " #\n", " for zi in range(1, int(z7)):\n", " theta=x4+dtheta*float(zi);\n", " x5=theta*theta;\n", " x6=4.*x5*x0*x0;\n", " x7=x2-x5;\n", " x8=np.sqrt(x7*x7+x6);\n", " x9=(x8+x7)/(2.*x0*x0);\n", " x10=2.*theta*dtheta*np.log(x9);\n", " eta=eta+x10;\n", " \n", " \n", " \n", " eta=eta+x2/100.*np.log(1.+x3) ; # addition of the central contribution\n", " x4=z5*z5*np.log(1.+x1*x1); # normalisation\n", " eta=eta/x4;\n", " #\n", " # correction by geometrical factor (beta/alpha)**2\n", " #\n", " if (z6\n", "
\n", " Figure\n", "
\n", " \n", " \n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "S_Xsection = eels.xsec_xrpa(energy_scale, 200, 16, 10. )/1e10 \n", "Mo_Xsection = eels.xsec_xrpa(energy_scale, 200, 42, 10. ,shift=0)/1e10 # xsec is in barns = 10^28 m2 = 10^10 nm2\n", "\n", "fig, ax1 = plt.subplots()\n", "\n", "ax1.plot(energy_scale, S_Xsection, label='S X-section' )\n", "ax1.plot(energy_scale, Mo_Xsection, label='Mo X-section' )\n", "ax1.set_xlabel('energy_loss [eV]')\n", "ax1.set_ylabel('probability [atoms/nm$^{2}$]')\n", "\n", "\n", "plt.legend();\n", "fig.tight_layout();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "\n", "The cross section is key to determine the chemical composition of an EELS spectrum. \n", "These cross sections are dependent on:\n", "- acceleration voltage\n", "- effective collection angle\n", "- element\n", "\n", "So these experimental parameters have to be provided for a calculations of cross sections.\n", "\n", "We will use these cross sections in the [chemical compostions notebook](CH4_08-Chemical_Composition.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Navigation\n", "- **Up Chapter 4: [Imaging](CH4_00-Spectroscopy.ipynb)** \n", "- **Back: [Dielectric Function](CH4_03-Drude.ipynb)** \n", "- **Next: [Chemical Composition](CH4_08-Chemical_Composition.ipynb)** \n", "- **List of Content: [Front](../_MSE672_Intro_TEM.ipynb)** \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.11.7" }, "toc": { "base_numbering": "7", "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "384px" }, "toc_section_display": true, "toc_window_display": true }, "vscode": { "interpreter": { "hash": "838e0debddb5b6f29d3d8c39ba50ae8c51920a564d3bac000e89375a158a81de" } } }, "nbformat": 4, "nbformat_minor": 4 }