Searching the database and comparing materialsΒΆ

../_images/Ag_n.png ../_images/Ag_k.png ../_images/Ag_GaAs_abs.png
    from solcore.absorption_calculator.nk_db import download_db, search_db
    from solcore import material
    from solcore import si
    from solcore.solar_cell import SolarCell
    from solcore.structure import Layer
    from solcore.solar_cell_solver import solar_cell_solver, default_options

    import numpy as np
    import matplotlib.pyplot as plt

    wl = si(np.arange(100, 900, 10), 'nm')

    opts = default_options
    opts.optics_method = 'TMM'
    opts.wavelength = wl

    # Download the database from This only needs to be done once.
    # Can specify the source URL and number of interpolation points.
    # download_db()

    # Can search the database to select an appropriate entry. Search by element/chemical formula.
    # In this case, look for silver.

    search_db('Ag', exact = True)
    # This prints out, line by line, matching entries. This shows us entries with
    # "pageid"s 0 to 14 correspond to silver.

# Let's compare the optical behaviour of some of those sources:
# (The pageid values listed are for the 2021-07-18 version of the database)
# pageid = 0, Johnson
# pageid = 2, Jiang
# pageid = 4, McPeak
# pageid = 10, Hagemann
# pageid = 14, Rakic (BB)

# create instances of materials with the optical constants from the database.
# The name (when using Solcore's built-in materials, this would just be the
# name of the material or alloy, like 'GaAs') is the pageid, AS A STRING, while
# the flag nk_db must be set to True to tell Solcore to look in the previously
# downloaded database from
Ag_Joh = material(name='0', nk_db=True)()
Ag_Jia = material(name='2', nk_db=True)()
Ag_McP = material(name='4', nk_db=True)()
Ag_Hag = material(name='10', nk_db=True)()
Ag_Rak = material(name='14', nk_db=True)()
Ag_Sol = material(name='Ag')() # Solcore built-in (from SOPRA)

names = ['Johnson', 'Jiang', 'McPeak', 'Hagemann', 'Rakic', 'Solcore built-in']

plt.plot(wl*1e9, np.array([Ag_Joh.n(wl), Ag_Jia.n(wl), Ag_McP.n(wl),
                           Ag_Hag.n(wl), Ag_Rak.n(wl), Ag_Sol.n(wl)]).T)
plt.xlabel("Wavelength (nm)")
plt.title("(2) $n$ and $\kappa$ values for Ag from different literature sources")

plt.plot(wl*1e9, np.array([Ag_Joh.k(wl), Ag_Jia.k(wl), Ag_McP.k(wl),
                           Ag_Hag.k(wl), Ag_Rak.k(wl), Ag_Sol.k(wl)]).T)
plt.xlabel("Wavelength (nm)")

# Compare performance as a back mirror on a GaAs 'cell'

# Solid line: absorption in GaAs
# Dashed line: absorption in Ag

GaAs = material('GaAs')()

colors = ['k', 'r', 'g', 'y', 'b', 'm']

for c, Ag_mat in enumerate([Ag_Joh, Ag_Jia, Ag_McP, Ag_Hag, Ag_Rak, Ag_Sol]):
    my_solar_cell = OptiStack([Layer(width=si('50nm'), material = GaAs)], substrate=Ag_mat)
    RAT = calculate_rat(my_solar_cell, wl*1e9, no_back_reflection=False)
    GaAs_abs = RAT["A_per_layer"][1]
    Ag_abs = RAT["T"]
    plt.plot(wl*1e9, GaAs_abs, color=colors[c], linestyle='-', label=names[c])
    plt.plot(wl*1e9, Ag_abs, color=colors[c], linestyle='--')

plt.xlabel("Wavelength (nm)")
plt.title("(3) Absorption in GaAs depending on silver optical constants")