Chapter 4: Spectroscopy
4.7. Energy-Loss Near-Edge Structure#
part of
Analysis of Transmission Electron Microscope Data
by Gerd Duscher, 2023
Microscopy Facilities
Joint Institute of Advanced Materials
The University of Tennessee, Knoxville
Model based analysis and quantification of data acquired with transmission electron microscopes
4.7.1. Content#
Retrieving and Plotting of reference EELS spectra from the EELS database
Discussion of the energy-loss near-edge structure (ELNES) of specific edges.
4.7.2. Load important packages#
4.7.2.1. Check Installed Packages#
import sys
from pkg_resources import get_distribution, DistributionNotFound
def test_package(package_name):
"""Test if package exists and returns version or -1"""
try:
version = get_distribution(package_name).version
except (DistributionNotFound, ImportError) as err:
version = '-1'
return version
# Colab setup ------------------
if 'google.colab' in sys.modules:
!pip install pyTEMlib -q
# pyTEMlib setup ------------------
else:
if test_package('sidpy') < '0.0.5':
print('installing sidpy')
!{sys.executable} -m pip install --upgrade pyTEMlib -q
if test_package('pyTEMlib') < '0.2021.4.20':
print('installing pyTEMlib')
!{sys.executable} -m pip install --upgrade pyTEMlib -q
# ------------------------------
print('done')
done
4.7.2.2. Import all relevant libraries#
Please note that the EELS_tools package from pyTEMlib is essential.
import sys
if 'google.colab' in sys.modules:
%gui qt
%matplotlib ipympl
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from scipy.ndimage.filters import gaussian_filter
## import the configuration files of pyTEMlib (we need access to the data folder)
import pyTEMlib
import pyTEMlib.file_tools as ft
import pyTEMlib.eels_tools as eels
# For archiving reasons it is a good idea to print the version numbers out at this point
print('pyTEM version: ',pyTEMlib.__version__)
You don't have igor installed. If you wish to open igor files, you will need to install it (pip install igor) before attempting.
You don't have gwyfile installed. If you wish to open .gwy files, you will need to install it (pip install gwyfile) before attempting.
Symmetry functions of spglib enabled
pyTEM version: 0.2023.4.0
4.7.3. Chemical Shift#
The chemical shift is the first feature that we discuss in respect of the shape or appearance of the ionization edges: the energy-loss near edge structure (ELNES). This section and the following one explain how to do simple analysis of near–edge features.
The chemical shift refers to small changes (up to a few eV) of the edge onset, and how this shift depends on the bonding of an element in a solid. Going back to figure in the Introduction to Core-Loss Spectra, we see that such a change can be caused by a change of the band-gap, in which case the final states are moving or by a movement of the core-levels (initial states).
Please note, that this explanation above is a simplification; what we measure is the energy difference of an excited atom to one in ground state. In the excited atom all states react to the new electronic configuration and not only the final and initial states. In fact, to calculate the energy-difference, one cannot use the difference between core-levels and bottom of the conduction band.
However, we want to know which of the above effects (band gap changes or core-level shift) is the major one, so that we can conclude back on bonding of the element in question.
As an example of chemical shift we look at reference data of the silicon L
4.7.3.1. Load reference data#
Si_L_reference_spectra = eels.get_spectrum_eels_db(element='Si',edge='L2,3')
a_SiO2_Si_L3_S_Schamm_63
a_SiO2_Si_L3_S_Schamm_64
Si_Si_L3_S_Schamm_58
Si3N4_Si_L3_Lingyang_Li_166
Si3N4_(alpha)_Si_L3_S_Schamm_61
Si3N4_(alpha)_Si_L3_S_Schamm_62
SiC(6H)_Si_L3_S_Schamm_65
SiC(6H)_Si_L3_S_Schamm_66
found 8 spectra in EELS database)
4.7.3.2. Plot silicon spectra#
plt.figure()
for name, spectrum in Si_L_reference_spectra.items():
if 'Core' in spectrum['TITLE'] or 'L3' in spectrum['TITLE']:
#plt.plot(spectrum['enery_scale'],spectrum['data']/np.max(spectrum['data']), label = spectrum['TITLE'])
pass
for name, spectrum in Si_L_reference_spectra.items():
if 'a_SiO2_Si_L3_S_Schamm_63' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Si-L$_{2,3}$: a-SiO$_2$')
if 'Si_Si_L3_S_Schamm_58' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Si-L$_{2,3}$: Si')
if 'Si3N4_(alpha)_Si_L3_S_Schamm_62' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Si-L$_{2,3}$: Si$_3$N$_4$')
if 'SiC(6H)_Si_L3_S_Schamm_66'in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Si-L$_{2,3}$: SiC')
plt.legend();
The shift of the edges as above can be caused by the intial and/or the final states.
4.7.3.3. Band gap#
The band gap changes are treated in the solid state theory of band structure and are, therefore, well covered in other textbooks. The trend is that with increased oxidation (or more general increased electronegativity of the reaction partner as in the series: B, C, N, O), the band gap opens and the edge shifts to higher energies.
This is seen in the figure above, where the onset of the Si-L
In fact, one can monitor band gap changes with stoichiometry at interfaces by the shift of the edge.
Please be aware that we see only the shift of the conduction band bottom and not the whole band gap change. This effect of the band gap is obvious between Si and SiO
4.7.3.4. Core-level shift#
The initial state, the core-level
, can also shift, for example after oxidation. Some electrons will transfer to an anion (for example oxygen) and less electrons are available to fill the band structure. This is shown below for the case of Cu and its two oxides Cu
The more electrons transfer to oxygen for the ionic bonding of these materials, the more the edges shift to lower energies, even though a band gap opens up. The opening up of the band gap will cause a shift too higher energies and counteracts the effect of ionization. Due to lower electron densities, at the Cu atoms in the oxides, the core levels are assumed to shift to higher energies (see below) and compensate a little for the effect.
The core-level shift is generally a small effect. This core states react to increase of electron density at the atom site with a decrease and vice versa. Simplified , we can think of the core level electrons getting repulsed from an increased electron density (through Coulomb interaction) and pushed closer (lower in energy) to the core.
Cu_L_reference_spectra = eels.get_spectrum_eels_db(element='Cu',edge='L2,3')
Cu_Cu_L3_Y_Kihn_124
Cu2O_Cu_L3_Y_Kihn_129
Profile Of Cu4O3-Cu L23
CuO_Cu_L3_Y_Kihn_127
found 4 spectra in EELS database)
plt.figure()
for name, spectrum in Cu_L_reference_spectra.items():
if 'Cu_Cu_L3_Y_Kihn_124' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Cu-L$_{2,3}$: Cu')
if 'CuO_Cu_L3_Y_Kihn_127' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Cu-L$_{2,3}$: CuO')
if 'Cu2O_Cu_L3_Y_Kihn_129' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'Cu-L$_{2,3}$: Cu$_2$O')
plt.legend();
plt.xlim(910, 980)
(910.0, 980.0)
The case of the oxidized Cu where the slightly oxidized Cu
This effect is even more pronounced for CuO with an larger band gap than Cu
In the figure below we see that the Si-L

We see that as we acquire spectra at the diffuse interface between Si and Ge, that the Si-L
This shift can be explained by a shift of core levels. An all electron calculations can determine the 2p

All electron ab initio calculations of the core level states for Si-Ge and Si-GaAs alloys with different composition. The calculations show an 2p
The shift of the core–levels to lower energies will increase the distance between core–level and conduction band bottom, which results in a simple picture in a shift to higher energies. We see that for pure Si, the 2p
All electrons will relax according to the overall electron density at the atom sites and the calculated core–level shifts can not be used for predicting the edge shifts. However, these calculations can explain the physical origin of the edge shift.
4.7.3.5. Conclusion#
In summation, we can say that the following effects (strongest first, weakest last) cause a chemical shift:
band gap opening
ionic bonding
core level shift
All of these effects can be present at once, but usually only one dominates the chemical shift.
4.7.4. White Line#
In this section, we try to analyze a distinct feature of the transition metal elements. The d-states of transition metal elements form a very flat band in the band structure. This flat band, creates a strong peak in the density of states. This analysis is based on the following simplification:
In the figure below, we see a s or p free electron like band in the general shape of a parabola, This parabola gives rise to a saw tooth like feature in the density of states (DOS) because flat bands have a higher density of states than steep ones. The DOS of the conduction band (above the Fermi level) is closely related to our EELS spectra. A flat d-band will cause a very prominent peak, a so called white line (in the age of photographic recording, these peaks appeared as white lines).

Since these d-bands are so prominent we can easily separate them from the rest. In figure \ref{fig:Ti-whiteline} we use the calculated cross–section as a model of the s and p free electron like states. After a subtraction, we get the pure d-band contribution.

A simple analysis of the white line ratios of Ti-L
Please, note that both the Ti-L
and Ti-L edge are split in two. We will discuss this crystal field splitting in chapter \ref{sec:Titanium} as an ELNES feature. Here we just consider the sum over the the whole Ti-L or/and Ti-L and ignore this splitting.
The deviation from the 2 : 1 white line ratio is assumed to be caused by J-J coupling, and is, therefore, symmetry dependent. The anomalous white line ratios have been used to determine valency of transition elements in compounds. Physically this approach is on shaky ground, because we do not know all the reasons for the change in the ratios, it has, however, been shown to be reliable for binary metallic alloys.
Fortunately, there is a easier method (from the physical point of view). We compare the total amount of white line intensities (which corresponds to the empty d-states) and normalize them by the free-electron gas like intensity beyond the white lines.
We use the method of Okamoto et al. \cite{Okamoto-Disko1992}.
The energy-window for the free-electron like part of the edge can be chosen arbitrarily and consistently. Following Okamoto et al., a 50 eV integration window should be used 50 eV beyond the edge onset. These will allow to compare the results to values in the literature. The edge has to be taken in very thin areas and if possible corrected for single scattering distribution, because otherwise the free electron like part contains plasmon contributions, which change the analysis.
For the above spectrum in the figure above, we get for the white line / free electron gas ratio (50 eV - 50 eV beyond edge onset) = 556 / 974 = 0.57. Ti in SrTiO
For example, the SrTiO
For TiO
In the plotof the Cu-L edges above, we can see that Cu has no empty d-states but with oxidation the d-bands get unoccupied and white lines appear. The more electrons get transferred to the oxygen neighbors, the more empty d-states and the more prominent the white lines will appear.
This analysis of the occupancy of d-states is extremely important for magnetic materials where the strength depends on the unpaired (d-band or f-band) electrons.
The same analysis can be done for the empty f bands of M–edges, which are also rather flat. Usually, the M
4.7.5. ELNES#
So far, we have only interpreted distinct features of the shape of the ionization edges. A general approach is to look at the shape of the edges in total and use this shape as a kind of fingerprint for the interpretation. Another one is to try to understand the different features as means of electronic structure calculations of various sophistication.
In order to understand the different approaches ( and their level of confidence in the results), we will discuss the most important edges one by one.
The shape of the ELNES is closely related to the density of states of the conduction band. The next chapters discuss the basics for an electronic structure interpretation of ELNES
4.7.5.1. Transition matrix and electronic structure#
The single scattering intensity of an ionization edge
\begin{equation} \Large J_k^1(E) \propto |M(E)|^2 N(E) \end{equation}
The transition matrix describes the transition probability between the core states and the final states (given by
The density of final states (conduction band) (
4.7.5.2. Life-time broadening#
For arbitrary excitations, the
\begin{equation} \Large \Gamma_i \pi_h \approx \hbar \end{equation}
The lifetime of the core -hole
Further broadening of the
Since the inelastic mean free path
The next two chapters discuss the density of final states
4.7.5.3. Dipole-selection rule#
Assuming a single electron approximation (and almost no electronic theory theory solves the many-particle problem fully) for the excitation, we can replace the many electron transition matrix elements with single electron matrix elements
\begin{equation} \Large M(\vec{q},E) = <f|\exp(i\vec{q}\vec{r})|i> \end{equation}
with the initial wave function
We now expand the operator
\begin{equation} \Large \exp(i\vec{q}\vec{r}) = 1 + i\vec{q}\vec{r} + higher-order\ terms \end{equation}
Because the final and initial wave functions must be orthogonal to each other, the first term gives zero. The higher–order terms are assumed to be negligible.
The second term has odd symmetry and determines the dipole selection rule:
if the initial and final wave functions are both even or odd, then the result is zero.
only if one of the initial and final wave are odd and one of them is even the result is not zero.
There is no transition probability for s to s transitions or p-p transitions or d-d or s-d transitions. However, transitions are possible from s to p states and from p to s and d states.
The (optical) dipole selection rule says the angular quantum number
For the dipole selection rule to be valid, obviously the higher order terms must be very small, otherwise a third integral (of the dipole–forbidden reflections) will modify the observed ELNES.
A rough estimates leads to the requirement
Therefore, we must use rather small aperture (
4.7.5.4. Core–hole effects and excitons#
So far, we can say that our ELNES can be calculated by the site and angular momentum projected density of states. This is true for almost all metals, but the core-hole effects (through excitonic interactions of the core–hole with the excited electron and valence electrons) further modifies the final density of states
The missing electron in the core hole and the extra electron in the conduction band causes changes in the band structure (in the core, valence and conduction states regions). These relaxation processes of all the electrons involved can be rather complicated and we have to know when these complications have to be applied.

As illustrated in the figure above (Pantelides, PRL 1975), if there is no influence of the core-electron, the edge onset of the ionization edge should be determined by: \begin{eqnarray} E_{onset} &=& E_{core-state} - E_{CB}\ &=& (E_{core-state} - E_{vacuum} ) + (E_{vacuum} - E_{CB} )\ &=& (E_{core-state} - E_{vacuum} ) + (E_{vacuum} - E_{VB} ) +(E_{VB} - E_{CB} ) \end{eqnarray}
The distances between core–states and vacuum and valence band (VB) and vacuum are directly accessible with XPS, while the band gap (
Therefore the calculated density of states not including core holes should start at that energy, If the agreement is not In all other cases, we must assume that the (excitonic) core-hole effects modify the
This modification of

The modifications of the core hole effect not be predicted without extensive calculations. Two methods have profen to be successful for ELNES calculations:
explicit core hole in an all electron calculation (like FLAPW)
Z+1 approximation in pseudo potential calculations (like VASP)
In the explicit core-hole approach, we just take one electron from a core level of one atom in the supercell and place it (energetically) on top of the other electrons of that (now excited) atom. Now, the electronic structure calculations give the ELNES as the local and angular projected density of states of the excited atom.
In the Z+1 calculation, the atom with the explicit core-hole is replaced by an atom with an atomic number increased by one. The core hole is simulated by an nuclear charge that is increased by one and the excess electron is there naturally. This Z+1 approximation can be used in combination with the much faster pseudo potential methods. The results of this approximation are very close to the explicit core hole simulations.
4.7.6. ELNES Examples#
4.7.6.1. Carbon#
Carbon has 4 valence electrons.\ As a free atom has two electron in 2s and two electron in 2p states.\ In a compound, it is easier to understand the bonding assuming that there is only one electron in the 2s state and three electrons in the 2p state.
Carbon can bond in three modes and as elemental material stays in two modification:
Diamond: sp
hybridizationlonsdaleite: sp
hybridizationGraphite: sp
hybridization
The energy levels of the 2s and 2p levels are so close together that they can hybridize (as many elements in the first row of the periodic table). Hybridization means the states are indistinguishable (in energy). Carbon is unique in a sense as it can hybridize so that 2, 3 or 4 electrons hybridize. The 1s (occupied by one electron) and two or all three p states hybridize into states with p character, which means they are directional and form covalent bonds.
In the case of the sp

Graphite has three covalent bonds and forms a layered structure with three nearest neighbors within the layers \ref{fig:sp2}. Within the layer, the
C_reference_spectra = eels.get_spectrum_eels_db(element='C',edge='K')
8-pm
8-pm
B4C_B_K_Otmane_Abidate_41
B4C_C_K_Otmane_Abidate_42
C_C_K_V_Serin_17
C_C_K_M__Jaouen,_G__Tourillon_20xray
C_C_K_M__Jaouen,_G__Tourillon_21xray
C_C_K_Otmane_Abidate_24
C_C_K_Odile_Stephan_226
C_C_K__243
C_(graphite)_C_K_David_Muller_(at_Cornell_University)_1995_23
7-pm
E113 250-2-cal-kgd-pm-dm
e113 k-pm
255-1-pmmod
C3N6H6_C_K_Susana_Trasobares_114
C3N6H6_C_K_Susana_Trasobares_115
CN_C_K_Susana_Trasobares_122
Cr3C2_C_K_R__Leapman_11
Cr7C3_C_K_R__Leapman_13
CrC_C_K_Christina_Scheu_176
FeCO3_O_K_A__Gloter,_A__Chen_206
MgCO3_O_K_Irmgard_Rom_43
MgCO3_C_K_Irmgard_Rom_44
SiC_beta_Si_K_Flank_30xray
found 24 spectra in EELS database)
plt.figure()
for name, spectrum in C_reference_spectra.items():
if 'Core' in spectrum['TITLE'] or 'L3' in spectrum['TITLE']:
#plt.plot(spectrum['enery_scale'],spectrum['data']/np.max(spectrum['data']), label = spectrum['TITLE'])
pass
for name, spectrum in C_reference_spectra.items():
if 'C_(graphite)_C_K_David_Muller_(at_Cornell_University)_1995_23' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'C-K: graphite')
if 'C_C_K_M__Jaouen,_G__Tourillon_21xray' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'C-K: diamond')
if 'C_C_K__243' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'C-K: ')
if 'CN_C_K_Susana_Trasobares_122' in spectrum['TITLE']:
plt.plot(spectrum['energy_scale'],spectrum['data']/np.max(spectrum['data']), label = 'C-K: CN')
plt.legend()
plt.xlim(275, 310)
(275.0, 310.0)
Looking at the bonding of carbon in molecular orbital theory or (its predecessor) Ligand–Field theory the non–hybridized p electron in graphite will form an occupied
Both spectra have a
The quantification of
4.7.6.2. Silicon#
The calculation of the transition matrix
We can, therefore, assume that only d-states form the Si-L

The edge onset of the Si-L
The main feature of the Si-L
This steep raise is another indication of the core–hole and must have its origin in an excitonic effect (an interaction of the excess electron in the conduction band an the hole in the core state). In the figure above, the calculations of an effective mass electron (due to an state that is created just below the conduction band) is compared to the experimental ELNES. Such an effective mass electron must be considered delocalized. We see that the raise is steep enough to explain the experimental raise, we also see that the effect (intensity) is not change the ELNES.
Only the explicit inclusion of the core–hole or the Z+1 calculations in figure \ref{fig:Si-L-pure2} can explain this steep onset. We can, therefore, conclude on localized excitonic enhancement of the states at the bottom of conduction band. This is a rather localized excitonic effect.
We can also see in the comparison of the explicit inclusion of the core-hole and the Z+1 approximation that both simulations lead to the same ELNES, however only the explicit core-hole calculation can predict the exact intensity (cross section) of the Si-L
The same calculations are also successful for SiO
4.7.6.3. Oxygen and Nitrogen#
Oxygen and nitrogen edges are usually very similar. Here we will discuss mostly the oxygen edge, but this can be easily transferred to nitrogen.
The Si-SiO
Another approach is the so called “finger-print” method. In this method, one compares edges from different but known materials and hopes that similar features are conclusive for different coordinations within the unknown material. This approach can be improved by using simulations of the ELNES of the edges as seen in the plot below.
O_reference_spectra = eels.get_spectrum_eels_db(element='O',edge='K')
(La0_7,Sr0_3)MnO3_O_K_imhoff_229
8-pm
Al2O3_Al_K_Flank_1xray
Al2O3_(alpha)_Al_K_Stefan_Nufer_53
AlPO4_Al_K_Flank_2xray
B2O3_B_K_Karine_Varlot_8xray
B2O3_B_K_Karine_Varlot_9xray
7-pm
E113 250-2-cal-kgd-pm-dm
e113 k-pm
255-1-pmmod
CaCoO NT-2
CaTiO3_Ti_L3_Saso_Sturm_133
CoO_O_K_dominique_IMHOFF_235
Cr2O3_(alpha)_O_K_Rik_Brydson_158
Cu2O_O_K_Y_Kihn_130
Profile Of Cu4O3-O-K
CuO_O_K_Y_Kihn_126
Fe2O3_(alpha)_O_K_A__Gloter,_A__Chen_201
Fe2TiO5_Ti_L3_E_Fries_P_Perriat_36
Fe3O4_O_K_A__Gloter,_A__Chen_205
FeCO3_O_K_A__Gloter,_A__Chen_206
FeOOH_O_K_A__Gloter,_A__Chen_204
FeOOH__(alpha)_O_K_A__Gloter,_A__Chen_203
FeOOH__(beta)_O_K_A__Gloter,_A__Chen_202
lsmo2
APL_article_LSMO_oxygen2
APL_article_interface_oxygen2
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EELS Spectrum Image (high-loss)model from decomposition with 6 components
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EELS Spectrum Image (high-loss)model from decomposition with 6 components
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EELS Spectrum Image (high-loss)model from decomposition with 6 components
LaCoO3_O_K_Irmgrad_Rom,_Ferdinand_Hofer_162
MgCO3_O_K_Irmgard_Rom_43
MgCO3_C_K_Irmgard_Rom_44
MgO_O_K_Giovanni_Bertoni_112
MgO_Mg_K_Giovanni_Bertoni_113
MgO_O_K_dominique_IMHOFF_233
MgO_Mg_K_dominique_IMHOFF_234
Na2O_Na_K_Flank_34xray
Nb2O5_Nb_M_David_Bach,_Wilfried_Sigle_215
NbO2_Nb_M_David_Bach,_Wilfried_Sigle_217
SiO2_Si_K_Flank_27xray
SiO2_Si_K_Flank_28xray
SiO2_Si_K_Flank_29xray
Spectre centre EELSDB
SrTiO3_O_K_imhoff_232
srtio32
APL_article_STO_oxygen2
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EELS Spectrum Image (high-loss)model from decomposition with 6 components
TiO2_(amorphous)_O_K_Giovanni_Bertoni_106
TiO2_(anatase)_O_K_Giovanni_Bertoni_103
Y3Al5O12_O_K_Saso_Sturm_182
Y3Al5O12_Al_K_Saso_Sturm_213
Y4Al2O9_O_K_Saso_Sturm_178
Y4Al2O9_Al_K_Saso_Sturm_179
YAlO3_O_K_Saso_Sturm_186
YAlO3_Al_K_Saso_Sturm_187
ZnO_O_K_Wilfried_Sigle_169
found 55 spectra in EELS database)
O_reference_titles = ['SrTiO3_O_K_imhoff_232',
'MgO_O_K_Giovanni_Bertoni_112',
'ZnO_O_K_Wilfried_Sigle_169',
'Cr2O3_(alpha)_O_K_Rik_Brydson_158'
]
O_reference_materials = ['SrTiO$_3$', 'MgO', 'ZnO', 'Cr$_2$O$_3$']
plt.figure()
i = 0
for name, spectrum in O_reference_spectra.items():
if spectrum['TITLE'] in O_reference_titles:
plt.plot(spectrum['enery_scale'],spectrum['data']/np.max(spectrum['data']), label = 'O-K:'+O_reference_materials[i])
i+=1
plt.legend();
plt.xlim(525,570)
(525.0, 570.0)
A comparison hows that the cubic oxides MgO and MgAl
Correlation effects of valence electrons cause the so called Hubbard band. These bands dominate the electronic structure in high T
In the figure below, we see that such a correlation effect takes place in the giant magneto-resistor LaMnO

The O-K edge in LaMnO
The same Hubbard band is visible in the high T

The O-K edge at a dislocation in YBa
The prepeak that is caused by the Hubbard band is reduced in the vicinity of the dislocation and vanishes completely within the dislocation core. This lack of holes in the dislocation disturbs the superconductivity and is utilized in grain boundaries for Josephson junctions but is detrimental in polycrystalline high T
4.7.7. Spatial Resolution in EELS#
4.7.7.1. Methods to achieve spatial resolution#
Spot Mode
Area Mode = Spatial Difference
Lines Scan Mode
Each Spot of Line one Spectrum
Each Segement of Line one Spectrum
Each Area of Line one Spectrum
Spectrum Imaging
4.7.7.2. Spot mode#
4.7.7.3. Spatial difference#
EELS at Bi doped Cu grain boundary
As segregation at Si/SiO
As segregation at Si/Sio
4.7.7.4. Energy Filtered Imaging (EFTEM)#
4.7.8. Summary#
The core–loss part of the electron energy–loss spectrum allows us to determine:
chemical composition
bonding
magnetic moment through ionic charge
with high spatial resolution!!!