#!/usr/bin/env python # coding: utf-8 #

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()