{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 3**\n",
    "\n",
    "**Problem 3.4**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The refractive index of silicon at 25°C can be found in the file “Refractive index silicon.csv” in the online repository of the book.**\n",
    "\n",
    "**(a) Calculate and plot the reflectance $r$ at the front surface of a silicon solar cell as a function of the photon wavelength.**\n",
    "\n",
    "**(b) Plot the QE of an ideal silicon solar cell and the QE of a silicon solar cell assuming that it is ideal except for the reflection losses. Discuss the results.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the package [pandas](https://pandas.pydata.org/) to handle the data and [matplotlib.pyplot](https://matplotlib.org/stable/index.html) to plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_Si_25C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nm</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1.6640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1.7540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>2.0870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>2.9610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>3.4928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1420</th>\n",
       "      <td>3.4917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>3.4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>3.4896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>3.4885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      n_Si_25C\n",
       "nm         NaN\n",
       "250     1.6640\n",
       "260     1.7540\n",
       "270     2.0870\n",
       "280     2.9610\n",
       "...        ...\n",
       "1410    3.4928\n",
       "1420    3.4917\n",
       "1430    3.4906\n",
       "1440    3.4896\n",
       "1450    3.4885\n",
       "\n",
       "[122 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafile = pd.read_csv('data/Refractive index silicon.csv', index_col=0, header=0) \n",
    "datafile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datafile.drop(datafile.index[0], inplace=True) #remove row including information on units\n",
    "datafile=datafile.astype(float) #convert values to float for easy operation\n",
    "datafile.index=datafile.index.astype(float) #convert indexees to float for easy operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the refectrative index spectra."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the reflectance at the air/silicon interface using Eq. 3.9 and plot it.\n",
    "\n",
    "$r=[\\frac{(n_{Si}-1)}{(n_{Si}+1)}]^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelength, $\\\\lambda$ (nm)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(datafile, \n",
    "         linewidth=2)\n",
    "plt.ylabel(r'Refractive index of sililcon, $n_{Si}$ @ 25 $\\degree$')\n",
    "plt.xlabel('Wavelength, $\\lambda$ (nm)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Wavelength, $\\\\lambda$ (nm)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=datafile['n_Si_25C'][datafile.index>=400]\n",
    "r=((n-1)/(n+1))**2\n",
    "plt.plot(r)\n",
    "plt.ylabel(r'Reflectance, $r$')\n",
    "plt.xlabel('Wavelength, $\\lambda$ (nm)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***(b) Plot the QE of an ideal silicon solar cell and the QE of a silicon solar cell assuming that it is ideal except for the reflection losses. Discuss the results.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ideal QE is 1 for wavelengths shorter than the bandgap wavelength $\\lambda_g$ and 0 for longer wavelengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bg_wl=1240/1.12\n",
    "QEone=lambda x: 1 if x < bg_wl else 0\n",
    "ideal_QE=pd.Series(index=r.index,\n",
    "                   data=[QEone(i) for i in r.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To account for the reflection losses, the ideal QE is multiplied by (1-$r$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "QE_ref_loss=pd.Series(index=ideal_QE.index,\n",
    "                    data=[(1-r.loc[i])*ideal_QE.loc[i] for i in ideal_QE.index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the ideal QE and the actual QE (affected by $r$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e3faa10708>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ideal_QE,\n",
    "         linewidth=2, label='ideal')\n",
    "plt.plot(QE_ref_loss,\n",
    "         linewidth=2, label='with reflection')\n",
    "plt.ylabel(r'Quantum Efficiency, $QE$')\n",
    "plt.xlabel('Wavelength, $\\lambda$ (nm)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Discussion***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reflecion losses reduce the QE and hence the photocurrent.<br>\n",
    "In silicon, these losses are higher than 30% for all wavelenghts, and are specially higher at short wavelenghts (<600 nm) becasue of the fast increase of the refractive index of silicon in that wavelength range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}