Refractive Index Information DB

In [ ]:
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
In [ ]:
wl = si(np.arange(100, 900, 10), 'nm')

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

Download the database from refractiveindex.info. This only needs to be done once. Can specify the source URL and number of interpolation points.

In [ ]:
download_db()

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

In [ ]:
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: pageid = 0, Johnson pageid = 2, McPeak pageid = 8, Hagemann pageid = 12, 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 refractiveindex.info

In [ ]:
Ag_Joh = material(name = '0', nk_db=True)()

Ag_McP = material(name = '2', nk_db=True)()
Ag_Hag = material(name = '8', nk_db=True)()
Ag_Rak = material(name = '12', nk_db=True)()

Ag_Sol = material(name = 'Ag')() # Solcore built-in (from SOPRA)

plot the n and k data. Note that not all the data covers the full wavelength range, so the n/k value stays flat.

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

plt.figure()
plt.plot(wl * 1e9, Ag_Joh.n(wl), wl * 1e9, Ag_McP.n(wl),
         wl * 1e9, Ag_Hag.n(wl), wl * 1e9, Ag_Rak.n(wl), wl * 1e9, Ag_Sol.n(wl))
plt.legend(labels=names)
plt.xlabel("Wavelength (nm)")
plt.ylabel("n")
plt.show()

plt.figure()
plt.plot(wl * 1e9, Ag_Joh.k(wl), wl * 1e9, Ag_McP.k(wl),
         wl * 1e9, Ag_Hag.k(wl), wl * 1e9, Ag_Rak.k(wl), wl * 1e9, Ag_Sol.k(wl))
plt.legend(labels=names)
plt.xlabel("Wavelength (nm)")
plt.ylabel("k")
plt.show()

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

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

In [ ]:
GaAs = material('GaAs')()

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

plt.figure()
for c, Ag_mat in enumerate([Ag_Joh, Ag_McP, Ag_Hag, Ag_Rak, Ag_Sol]):
    my_solar_cell = SolarCell([Layer(width=si('50nm'), material = GaAs)] +
                            [Layer(width = si('100nm'), material = Ag_mat)])
    solar_cell_solver(my_solar_cell, 'optics', opts)
    GaAs_positions = np.linspace(my_solar_cell[0].offset, my_solar_cell[0].offset + my_solar_cell[0].width, 1000)
    Ag_positions = np.linspace(my_solar_cell[1].offset, my_solar_cell[1].offset + my_solar_cell[1].width, 1000)
    GaAs_abs = np.trapz(my_solar_cell[0].diff_absorption(GaAs_positions), GaAs_positions)
    Ag_abs = np.trapz(my_solar_cell[1].diff_absorption(Ag_positions), Ag_positions)
    plt.plot(wl*1e9, GaAs_abs, color=colors[c], linestyle='-', label=names[c])
    plt.plot(wl*1e9, Ag_abs, color=colors[c], linestyle='--')

plt.legend()
plt.xlabel("Wavelength (nm)")
plt.ylabel("Absorbed")
plt.show()