Transport properties plot#

[1]:

from CRYSTALClear.crystal_io import *
from plot import *

Read a seebeck files

[2]:

seebeck = Properties_output().read_cry_seebeck('transport_zrnisn_SEEBECK.DAT')

[3]:

seebeck1 = Properties_output().read_cry_seebeck('transport_zrnisn_antisito_SEEBECK.DAT')

[4]:

?plot_cry_seebeck_potential

Signature: plot_cry_seebeck_potential(seebeck_obj, direction, temperature)
Docstring:
Plot the Seebeck coefficient as a function of chemical potential.

Args:
    seebeck_obj (object): Seebeck object containing the data for the Seebeck coefficient.
    direction (str): choose the direction to plot among 'S_xx', 'S_xy', 'S_xz', 'S_yx', 'S_yy', 'S_yz', 'S_yz', 'S_zx', 'S_zy', 'S_zz'.
    temperature (value/str): choose the temperature to be considered or 'all' to consider them all together

Returns:
    Figure object

Notes:
    - Plots the Seebeck coefficient as a function of chemical potential for each temperature.
    - Distinguishes between n-type and p-type conduction with dashed and solid lines, respectively.
File:      c:\users\ascri\onedrive\desktop\dottorato_secondo_anno\tutorial_2022\electron_transport_properties\plot.py
Type:      function

[5]:

grafico = plot_cry_seebeck_carrier(seebeck,'s_xx','all')

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_6_1.png

<Figure size 640x480 with 0 Axes>

[7]:

grafico.savefig('seebeck_potential',dpi=300)

Plot seebeck versus the charge carrier concentration

[9]:

plot_cry_seebeck_carrier(seebeck,'s_xx',300)

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_9_1.png

[9]:

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

Read a sigma files

[10]:

sigma = Properties_output().read_cry_sigma('transport_zrnisn_SIGMA.DAT')

[11]:

sigma1 = Properties_output().read_cry_sigma('transport_zrnisn_antisito_SIGMA.DAT')

Plot sigma versus the chemical potential

[12]:

plot_cry_sigma_potential(sigma,'s_xx','all')

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_14_1.png

[12]:

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

Plot multiseebeck

[14]:

?plot_cry_multiseebeck

Signature: plot_cry_multiseebeck(direction, temperature, minpot, maxpot, *seebeck)
Docstring:
Plot the seebeck coefficient from different files.

Args:
    direction (str): choose the direction to plot among 'S_xx', 'S_xy', 'S_xz', 'S_yx', 'S_yy', 'S_yz', 'S_yz', 'S_zx', 'S_zy', 'S_zz'.
    temperature (value): choose the temperature to be considered
    minpot (value): lower value of chemical potential you want to plot in eV
    maxpot (value): higher value of chemical potential you want to plot in eV
    *seebeck (obj): Variable number of seebeck objects containing the data for the Seebeck coefficient.

Returns:
    Figure object

Notes:

    - Plots the seebeck coefficient for each seebeck object.
    - Differentiates transport coefficients due to n-type or p-type conduction using dashed and solid lines.
File:      c:\users\ascri\onedrive\desktop\dottorato_secondo_anno\tutorial_2022\electron_transport_properties\plot.py
Type:      function

[15]:

plot_cry_multiseebeck('s_xx',300,-6,-1,seebeck, seebeck1,)

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_17_1.png

[15]:

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

Plot multisigma

[17]:

plot_cry_multisigma('s_xx',300,-6,-1,sigma, sigma1)

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_19_1.png

[17]:

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

Plot powerfactor

[18]:

plot_cry_powerfactor_carrier(seebeck, sigma,'pf_xx','all')

To differentiate transport coefficients due to n-type or p-type conduction (electrons or holes as majority carriers) dashed and solid lines are used, respectively.

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_21_1.png

[18]:

<function matplotlib.pyplot.gcf() -> 'Figure'>

Plot ZT

[19]:

?plot_cry_zt

Signature: plot_cry_zt(seebeck_obj, sigma_obj, direction, temperature, ktot)
Docstring:
Plot the ZT value for different temperatures.

Args:
    seebeck_obj (obj): Seebeck object containing the data for the Seebeck coefficient.
    sigma_obj (obj): Sigma object containing the data for the electrical conductivity.
    direction (str): choose the direction to plot among 'ZT_xx', 'ZT_xy', 'ZT_xz', 'ZT_yx', 'ZT_yy', 'ZT_yz', 'ZT_yz', 'ZT_zx', 'ZT_zy', 'ZT_zz'.
    temperature (value/str): choose the temperature to be considered or 'all' to consider them all together
    ktot (value): alue of the total thermal conductivity (ktot) in W-1K-1m-1

Returns:
    Figure object

Notes:
    - Calculates the ZT value using the Seebeck coefficient and electrical conductivity data.
    - Plots the ZT value for each temperature as a function of the chemical potential.
File:      c:\users\ascri\onedrive\desktop\dottorato_secondo_anno\tutorial_2022\electron_transport_properties\plot.py
Type:      function

[20]:

plot_cry_zt(seebeck,sigma,'zt_xx','all',10)

../../_images/notebooks_electron_transport_properties_tutorial_electron_transport_properties_24_0.png

[20]:

<function matplotlib.pyplot.gcf() -> 'Figure'>

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