{
"cells": [
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"id": "0rmnHFFLAbuK"
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"source": [
" **[MSE672: Introduction to TEM](https://gduscher.github.io/MSE672-Introduction-to-TEM/)** \n",
"\n",
"
\n",
"\n",
"# Overview Notebook\n",
"\n",
"[Download](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM//main/Introduction/CH1_06-Overview.ipynb)\n",
"\n",
"[](\n",
" https://colab.research.google.com/github/gduscher/MSE672-Introduction-to-TEM/blob/main/Introduction/CH1_06-Overview.ipynb)\n",
"\n",
"\n",
" part of\n",
"\n",
" **[MSE672: Introduction to Transmission Electron Microscopy](../_MSE672_Intro_TEM.ipynb)**\n",
"\n",
"**Spring 2026**
\n",
"by Gerd Duscher\n",
"\n",
"Microscopy Facilities
\n",
"Institute of Advanced Materials & Manufacturing
\n",
"Materials Science & Engineering
\n",
"The University of Tennessee, Knoxville\n",
"\n",
"\n",
"\n",
"## Import packages for figures and \n",
"First we load the code to make figures from pyTEMlib\n",
"### Check Installed Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"installing pyTEMlib\n",
"done\n"
]
}
],
"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') < '0.2026.1.2':\n",
" print('installing pyTEMlib')\n",
" !{sys.executable} -m pip install --upgrade pyTEMlib -q\n",
"print('done')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the plotting and figure packages\n",
"\n",
">Note for Google Colab\n",
">\n",
">The runtime has to be restarted before you run the code cell below to enable interactive plotting.\n",
">\n",
">In the Menu **Runtime** choose **Restart Runtime** (**Ctrl-M**)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib widget\n",
"\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"import sys\n",
"if 'google.colab' in sys.modules:\n",
" from google.colab import output\n",
" output.enable_custom_widget_manager()\n",
"\n",
"import pyTEMlib.animation as animate"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yLZQEdjYUGNO"
},
"source": [
"## History\n",
"\n",
"\n",
"| | | |\n",
"| :------------- | :----------: | -----------: |\n",
"| 1925 | Louis de Broglie | electron has a wavelike character with a wavelength less than light|\n",
"|1927 | Davisson and Germer | classic electron diffraction experiments|\n",
"| | Thompson and Reed||\n",
"|1932 |Knoll and Ruska| first electron lenses and first image (Noble Price 1986)|\n",
"|1936 |Vickers| first commercial electron TEM|\n",
"|1939 |Siemens and Halske| first usable and profitable TEM|\n",
"|1949 |Heidenreich| first transparent metal foil (first materials science result)|\n",
"|2000 |Krivanek |(STEM) first prototypes for spherical aberration|\n",
"||Rose/Heider| (TEM) objective-lens correctors|\n",
"|2007 |Krivanek |(STEM) first prototypes for fifth order|\n",
"| | Rose/Heider| (TEM) aberration objective-lens correctors|\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zDbWs5s0AC1l"
},
"source": [
"## Available TEMs\n",
"TEMs are now available from many sources ( for example: FEI, Hitachi,\n",
"JEOL, Nion).\n",
"## TEMs available in the UTK/ORNL area.\n",
"- Zeiss Libra200 MC at UTK\n",
"- ThermoFischer Spectra300 at UTK 5th order corrected\n",
"- JEOL 2100+ at Tennessee Ion Beam Materials Laboratory/UTK\n",
"- Nion UltraSTEM 100 at ORNL/CNMS, 5th order corrected\n",
"- JEOL 2100 at ORNL 3rd order corrected\n",
"- FEI Titan at ORNL/CNMS, 3rd order corrected\n",
"- JEOL ARM at ORNL/CNMS, 5th order corrected\n",
"- Nion UltraSTEM 200 at ORNL, 5th order corrected\n",
"- NionMACSTEM 100 at ORNL, 5th order corrected\n",
"- FEI Talos F200X S/TEM at ORNL,\n",
"- Hitachi 300 at ORNL\n",
"\n",
"The dedicated STEMs are all equipped with a spherical aberration\n",
"corrector by Nion. There are also four CEOS (Haider and Rose)\n",
"aberration corrected TEMs (ThermoFisher and JEOL).\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uSRkqw7WX7L_"
},
"source": [
"## Electron Meets Matter\n",
"To gather information about a sample the electron has to interact with\n",
"this sample, otherwise it would be invisible. There is a whole zoo of\n",
"interactions. The primary and most\n",
"important interaction for TEM imaging is (elastic and inelastic) scattering. All the other\n",
"processes are secondary (for example: X-ray emission).\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bX5zOk-tYdNY"
},
"source": [
"## Diffraction and Imaging\n",
"\n",
"**No interaction**\n",
"\n",
"Most electrons do not interact with a thin specimen at all.\n",
"\n",
"**Interaction without energy transfer**\n",
"\n",
"Elastic scattering is the basis for diffraction and imaging.\n",
"\n",
"**Interaction with energy transfer**\n",
"\n",
"Inelastic scattering causes a diffuse background in images and diffraction\n",
"pattern, but can be used for analytical TEM."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T7L7Z_aXY46E"
},
"source": [
"## Modes of a TEM\n",
"\n",
"Some techniques:\n",
"\n",
"| | |\n",
"|:----:|:---------|\n",
"|SAED |selected area electron diffraction|\n",
"|CBED| convergent beam electron diffraction|\n",
"|Kikuchi |Kikuchi diffraction|\n",
"|Fresnel |Fresnel diffraction|\n",
"|CTEM |conventional transmission electron microscopy|\n",
"|BF |bright field imaging|\n",
"|DF |dark field imaging|\n",
"|HRTEM| high resolution (phase contrast) |transmission electron microscopy|\n",
"|SE |secondary electron imaging|\n",
"|BE| backscatter electron imaging|\n",
"|Lorentz |Lorentz microscopy|\n",
"|HAADF |high angle annular dark field imaging (Z-contrast)|"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iD4-bYe8Zr6g"
},
"source": [
"## Modes of a TEM/STEM\n",
"| |illumination| objective| projective |I projective II|\n",
"|:---:|:---:|:---:|:---:|:---:|\n",
"|TEM |TEM | Mag|Image |ESI|\n",
"|Nanoprobe |Spot| Mag| Image| ESI|\n",
"|LowMag |TEM | LowMag| Image| ESI|\n",
"|Microprobe| Spot| LowMag| Image| ESI|\n",
"|SAED| TEM| Mag| Diffr| ESI|\n",
"|Low angle diffraction |TEM |LowMag| Diffr| ESI|\n",
"|CBED |Spot |Mag |Diffr| ESI|\n",
"|LACBED |Spot| LowMag| Diffr| ESI|\n",
"|Spectroscopy |TEM |Mag |Image| EELS|\n",
"|Spectroscopy |Spot| Mag |Diffr| EELS|\n",
"|STEM |Spot |Mag |Diffr| ESI|\n",
"|STEM-LM |Spot |LowMag| Diffr| ESI|\n",
"|STEM-SI| Spot| Mag| Diffr| EELS|\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1thHpagca_fs"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The main ingredients in an TEM method are how is the sample ``illuminated`` (condenser) and how are the electrons ``sorted`` and ``selected`` (projector and spectrometer)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diffraction and Imaging\n",
"Even though we are dealing with relativistic and quantum mechanical principles the geometric ray diagrams are essential for an understanding of how to set up a TEM mode."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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",
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