Energy-Dispersive X-Rays Spectrometry (EDS)

The methods described here are specific to the following signals:

This chapter described step by step the analysis of an EDS spectrum (SEM or TEM).

Note

See also the EDS tutorials .

Spectrum loading and parameters

Data files used in the following examples can be downloaded using

>>> from urllib import urlretrieve
>>> url = 'http://cook.msm.cam.ac.uk//~hyperspy//EDS_tutorial//'
>>> urlretrieve(url + 'Ni_superalloy_1pix.msa', 'Ni_superalloy_1pix.msa')
>>> urlretrieve(url + 'Ni_superalloy_010.rpl', 'Ni_superalloy_010.rpl')
>>> urlretrieve(url + 'Ni_superalloy_010.raw', 'Ni_superalloy_010.raw')

Note

The sample and the data used in this chapter are described in P. Burdet, et al., Acta Materialia, 61, p. 3090-3098 (2013) (see abstract).

Loading

All data are loaded with the load() function, as described in details in Loading files. HyperSpy is able to import different formats, among them ”.msa” and ”.rpl” (the raw format of Oxford Instrument and Brucker).

Here is three example for files exported by Oxford Instrument software (INCA). For a single spectrum:

>>> s = hs.load("Ni_superalloy_1pix.msa")
>>> s
<Signal1D, title: Signal1D, dimensions: (|1024)>

For a spectrum image (The .rpl file is recorded as an image in this example, The method as_signal1D() set it back to a one dimensional signal with the energy axis in first position):

>>> si = hs.load("Ni_superalloy_010.rpl").as_spectrum(0)
>>> si
<Signal1D, title: , dimensions: (256, 224|1024)>

For a stack of spectrum images (The “*” replace all chains of string, in this example 01, 02, 03,...):

>>> si4D = hs.load("Ni_superalloy_0*.rpl", stack=True)
>>> si4D = si4D.as_signal1D(0)
>>> si4D
<Signal1D, title:, dimensions: (256, 224, 2|1024)>

Microscope and detector parameters

First, the type of microscope (“EDS_TEM” or “EDS_SEM”) needs to be set with the set_signal_type() method. The class of the object is thus assigned, and specific EDS methods become available.

>>> s = hs.load("Ni_superalloy_1pix.msa")
>>> s.set_signal_type("EDS_SEM")
>>> s
<EDSSEMSpectrum, title: Spectrum, dimensions: (|1024)>

or as an argument of the load() function:

>>> s = hs.load("Ni_superalloy_1pix.msa", signal_type="EDS_SEM")
>>> s
<EDSSEMSpectrum, title: Spectrum, dimensions: (|1024)>

The main values for the microscope parameters are automatically imported from the file, if existing. The microscope and detector parameters are stored in stored in the metadata attribute (see Metadata structure). These parameters can be displayed as follow:

>>> s = hs.load("Ni_superalloy_1pix.msa", signal_type="EDS_SEM")
>>> s.metadata.Acquisition_instrument.SEM
├── Detector
│   └── EDS
│       ├── azimuth_angle = 63.0
│       ├── elevation_angle = 35.0
│       ├── energy_resolution_MnKa = 130.0
│       ├── live_time = 0.006855
│       └── real_time = 0.0
├── beam_current = 0.0
├── beam_energy = 15.0
└── tilt_stage = 38.0

These parameters can be set directly:

>>> s = hs.load("Ni_superalloy_1pix.msa", signal_type="EDS_SEM")
>>> s.metadata.Acquisition_instrument.SEM.beam_energy = 30

or with the set_microscope_parameters() method:

>>> s = hs.load("Ni_superalloy_1pix.msa", signal_type="EDS_SEM")
>>> s.set_microscope_parameters(beam_energy = 30)

or raising the gui:

>>> s = hs.load("Ni_superalloy_1pix.msa", signal_type="EDS_SEM")
>>> s.set_microscope_parameters()
../_images/EDS_microscope_parameters_gui.png

EDS microscope parameters preferences window.

If the microscope and detector parameters are not written in the original file, some of them are set by default. The default values can be changed in the Preferences class (see preferences).

>>> hs.preferences.EDS.eds_detector_elevation = 37

or raising the gui:

>>> hs.preferences.gui()
../_images/EDS_preferences_gui.png

EDS preferences window.

Energy axis

The main values for the energy axis are automatically imported from the file, if existing. The properties of the energy axis can be set manually with the AxesManager. (see Axis properties for more info):

>>> si = hs.load("Ni_superalloy_010.rpl", signal_type="EDS_TEM").as_spectrum(0)
>>> si.axes_manager[-1].name = 'E'
>>> si.axes_manager['E'].units = 'keV'
>>> si.axes_manager['E'].scale = 0.01
>>> si.axes_manager['E'].offset = -0.1

or with the gui() method:

>>> si.axes_manager.gui()
../_images/EDS_energy_axis_gui.png

Axis properties window.

Describing the sample

The description of the sample is stored in metadata.Sample (in the metadata attribute). It can be displayed as follow:

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> s.metadata.Sample.thickness = 100
>>> s.metadata.Sample
├── description = FePt bimetallic nanoparticles
├── elements = ['Fe', 'Pt']
├── thickness = 100
└── xray_lines = ['Fe_Ka', 'Pt_La']

The following methods are either called “set” or “add”. When “set” methods erases all previously defined values, the “add” methods add the values to the previously defined values.

Elements

The elements present in the sample can be defined with the set_elements() and add_elements() methods. Only element abbreviations are accepted:

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.set_elements(['Fe', 'Pt'])
>>> s.add_elements(['Cu'])
>>> s.metadata.Sample
└── elements = ['Cu', 'Fe', 'Pt']

X-ray lines

Similarly, the X-ray lines can be defined with the set_lines() and add_lines() methods. The corresponding elements will be added automatically. Several lines per elements can be defined.

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.set_elements(['Fe', 'Pt'])
>>> s.set_lines(['Fe_Ka', 'Pt_La'])
>>> s.add_lines(['Fe_La'])
>>> s.metadata.Sample
├── elements = ['Fe', 'Pt']
└── xray_lines = ['Fe_Ka', 'Fe_La', 'Pt_La']

These methods can be used automatically, if the beam energy is set. The most excited X-ray line is selected per element (highest energy above an overvoltage of 2 (< beam energy / 2)).

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.set_elements(['Al', 'Cu', 'Mn'])
>>> s.set_microscope_parameters(beam_energy=30)
>>> s.add_lines()
>>> s.metadata.Sample
├── elements = ['Al', 'Cu', 'Mn']
└── xray_lines = ['Al_Ka', 'Cu_Ka', 'Mn_Ka']

>>> s.set_microscope_parameters(beam_energy=10)
>>> s.set_lines([])
>>> s.metadata.Sample
├── elements = ['Al', 'Cu', 'Mn']
└── xray_lines = ['Al_Ka', 'Cu_La', 'Mn_La']

A warning is raised, if setting a X-ray lines higher than the beam energy.

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.set_elements(['Mn'])
>>> s.set_microscope_parameters(beam_energy=5)
>>> s.add_lines(['Mn_Ka'])
Warning: Mn Ka is above the data energy range.

Element database

An elemental database is available with the energy of the X-ray lines.

>>> hs.material.elements.Fe.General_properties
├── Z = 26
├── atomic_weight = 55.845
└── name = iron
>>> hs.material.elements.Fe.Physical_properties
└── density (g/cm^3) = 7.874
>>> hs.material.elements.Fe.Atomic_properties.Xray_lines
├── Ka
│   ├── energy (keV) = 6.404
│   └── weight = 1.0
├── Kb
│   ├── energy (keV) = 7.0568
│   └── weight = 0.1272
├── La
│   ├── energy (keV) = 0.705
│   └── weight = 1.0
├── Lb3
│   ├── energy (keV) = 0.792
│   └── weight = 0.02448
├── Ll
│   ├── energy (keV) = 0.615
│   └── weight = 0.3086
└── Ln
    ├── energy (keV) = 0.62799
    └── weight = 0.12525

Plotting

As decribed in visualisation, the plot() method can be used:

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.plot()
../_images/EDS_plot_spectrum.png

EDS spectrum.

An example of plotting EDS data of higher dimension (3D SEM-EDS) is given in visualisation multi-dimension.

Plot X-ray lines

New in version 0.8.

X-ray lines can be labbeled on a plot with plot(). The lines are either given, either retrieved from “metadata.Sample.Xray_lines”, or selected with the same method as add_lines() using the elements in “metadata.Sample.elements”.

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.add_elements(['C','Mn','Cu','Al','Zr'])
>>> s.plot(True)
../_images/EDS_plot_Xray_default.png

EDS spectrum plot with line markers.

Selecting certain type of lines:

>>> s = hs.datasets.example_signals.EDS_SEM_Spectrum()
>>> s.add_elements(['C','Mn','Cu','Al','Zr'])
>>> s.plot(True, only_lines=['Ka','b'])
../_images/EDS_plot_Xray_a.png

EDS spectrum plot with a selection of line markers.

Get lines intensity

Data files used in the following examples can be downloaded using

>>> from urllib import urlretrieve
>>> url = 'http://cook.msm.cam.ac.uk//~hyperspy//EDS_tutorial//'
>>> urlretrieve(url + 'core_shell.hdf5', 'core_shell.hdf5')

Note

The sample and the data used in this section are described in D. Roussow et al., Nano Lett, 10.1021/acs.nanolett.5b00449 (2015).

New in version 0.8.

The width of integration is defined by extending the energy resolution of Mn Ka to the peak energy (“energy_resolution_MnKa” in metadata).

>>> s = hs.load('core_shell.hdf5')
>>> s.get_lines_intensity(['Fe_Ka'], plot_result=True)
../_images/EDS_get_lines_intensity.png

Iron map as computed and displayed by get_lines_intensity.

The X-ray lines defined in “metadata.Sample.Xray_lines” (see above) are used by default.

>>> s = hs.load('core_shell.hdf5')
>>> s.set_lines(['Fe_Ka', 'Pt_La'])
>>> s.get_lines_intensity()
[<Signal2D, title: X-ray line intensity of Core shell: Fe_Ka at 6.40 keV, dimensions: (|64, 64)>,
<Signal2D, title: X-ray line intensity of Core shell: Pt_La at 9.44 keV, dimensions: (|64, 64)>]

The windows of integration can be visualised using plot() method

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()[5.:13.]
>>> s.add_lines()
>>> s.plot(integration_windows='auto')
../_images/EDS_integration_windows.png

EDS spectrum with integration windows markers.

Background subtraction

New in version 0.8.

The background can be subtracted from the X-ray intensities with the get_lines_intensity() method. The background value is obtained by averaging the intensity in two windows on each side of the X-ray line. The position of the windows can be estimated with the estimate_background_windows() method and can be plotted with the plot() method as follow. The integration windows are plotted with dashed lines.

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()[5.:13.]
>>> s.add_lines()
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> s.plot(background_windows=bw)
>>> s.get_lines_intensity(background_windows=bw, plot_result=True)
../_images/EDS_background_subtraction.png

EDS spectrum with background subtraction markers.

Quantification

New in version 0.8.

One TEM quantification method (Cliff-Lorimer) is implemented so far.

Quantification can be applied from the intensities (background subtracted) with the quantification() method. The required k-factors can be usually found in the EDS manufacturer software.

>>> s = hs.datasets.example_signals.EDS_TEM_Spectrum()
>>> s.add_lines()
>>> kfactors = [1.450226, 5.075602] #For Fe Ka and Pt La
>>> bw = s.estimate_background_windows(line_width=[5.0, 2.0])
>>> intensities = s.get_lines_intensity(background_windows=bw)
>>> weight_percent = s.quantification(intensities, kfactors, plot_result=True)
Fe (Fe_Ka): Composition = 4.96 weight percent
Pt (Pt_La): Composition = 95.04 weight percent

The obtained composition is in weight percent. It can be changed transformed into atomic percent either with the option quantification():

>>> # With s, intensities and kfactors from before
>>> s.quantification(intensities, kfactors, plot_result=True,
>>>                  composition_units='atomic')
Fe (Fe_Ka): Composition = 15.41 atomic percent
Pt (Pt_La): Composition = 84.59 atomic percent

either with weight_to_atomic(). The reverse method is atomic_to_weight().

>>> # With weight_percent from before
>>> atomic_percent = hs.material.weight_to_atomic(weight_percent)