Electron Energy Loss Spectroscopy¶

Tools for EELS data analysis¶

The functions described in this chapter are only available for the EELSSpectrum class. To transform a BaseSignal (or subclass) into a EELSSpectrum:

>>> s.set_signal_type("EELS")

Note these chapter discusses features that are available only for EELSSpectrum class. However, this class inherits many useful feature from its parent class that are documented in previous chapters.

EELS curve fitting¶

HyperSpy makes it really easy to quantify EELS core-loss spectra by curve fitting as it is shown in the next example of quantification of a boron nitride EELS spectrum from the The EELS Data Base.

Load the core-loss and low-loss spectra

>>> s = hs.load("BN_(hex)_B_K_Giovanni_Bertoni_100.msa")
>>> ll = hs.load("BN_(hex)_LowLoss_Giovanni_Bertoni_96.msa")

Set some important experimental information that is missing from the original core-loss file

>>> s.set_microscope_parameters(beam_energy=100, convergence_angle=0.2, collection_angle=2.55)

Define the chemical composition of the sample

>>> s.add_elements(('B', 'N'))

In order to include the effect of plural scattering we provide the low-loss spectrum to create_model():

>>> m = s.create_model(ll=ll)

HyperSpy has created the model and configured it automatically:

>>> m.components
   # |            Attribute Name |            Component Name |            Component Type
---- | ------------------------- | ------------------------- | -------------------------
   0 |                  PowerLaw |                  PowerLaw |                  PowerLaw
   1 |                       N_K |                       N_K |                EELSCLEdge
   2 |                       B_K |                       B_K |                EELSCLEdge

Conveniently, all the EELS core-loss components of the added elements are added automatically, names after its element symbol.

By default the fine structure features are disabled (although the default value can be configured (see Configuring HyperSpy). We must enable them to accurately fit this spectrum.

>>> m.enable_fine_structure()

We use smart_fit instead of standard fit method because smart_fit is optimized to fit EELS core-loss spectra

>>> m.smart_fit()

This fit can also be applied over the entire signal to fit a whole spectrum image

>>> m.multifit(kind='smart')

Print the result of the fit

>>> m.quantify()
Absolute quantification:
Elem.       Intensity
B   0.045648
N   0.048061

Visualize the result

>>> m.plot()

There are several methods that are only available in EELSModel:

• smart_fit() is a fit method that is more robust than the standard routine when fitting EELS data.
• quantify() prints the intensity at the current locations of all the EELS ionisation edges in the model.
• remove_fine_structure_data() removes the fine structure spectral data range (as defined by the fine_structure_width) ionisation edge components. It is specially useful when fitting without convolving with a zero-loss peak.

The following methods permit to easily enable/disable background and ionisation edges components:

• enable_edges()
• enable_background()
• disable_background()
• enable_fine_structure()
• disable_fine_structure()

The following methods permit to easily enable/disable several ionisation edge functionalities:

• set_all_edges_intensities_positive()
• unset_all_edges_intensities_positive()
• enable_free_onset_energy()
• disable_free_onset_energy()
• fix_edges()
• free_edges()
• fix_fine_structure()
• free_fine_structure()

When fitting edges with fine structure enabled it is often desirable that the fine structure region of nearby ionization edges does not overlap. HyperSpy provides a method, resolve_fine_structure(), to automatically adjust the fine structure to prevent fine structure to avoid overlapping. This method is executed automatically when e.g. components are added or removed from the model, but sometimes is necessary to call it manually.