{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 12.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fundamentals of Solar Cells and Photovoltaic Systems Engineering**\n",
    "\n",
    "**Solutions Manual - Chapter 12**\n",
    "\n",
    "**Problem 12.7**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the QE of a silicon solar cell, calculate the photogenerated current density $J_L$ under the reference spectrum AM1.5G and compare it to the $J_L$ obtained from irradiance spectra produced by a Xe-arc lamp and a quartz tungsten halogen lamp. Then, calculate the error produced using a solar simulator based on Xe-arc or quartz tungsten lamps. The tabulated spectra and QE are provided in the online repository of this book.**\n",
    "\n",
    "**Note that the error may be large because the Xe-arc and the quartz W halogen lamps are not placed at the right distance from the measuring plane. Let us assume that the spectral irradiance of the two lamps decreases homogeneously by 1% throughout the whole wavelength range for each cm of separation that is added between the lamp and the measuring plane. How far should each lamp be located with respect to the initial position to produce the same $J_L$ that the AM1.5G solar?**\n"
   ]
  },
  {
   "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 for the solar spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>AM0</th>\n",
       "      <th>AM1.5G</th>\n",
       "      <th>AM1.5D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Wvlgth nm</th>\n",
       "      <td>Etr W*m-2*nm-1</td>\n",
       "      <td>Global tilt  W*m-2*nm-1</td>\n",
       "      <td>Direct+circumsolar W*m-2*nm-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>8.20E-02</td>\n",
       "      <td>4.73E-23</td>\n",
       "      <td>2.54E-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280.5</th>\n",
       "      <td>9.90E-02</td>\n",
       "      <td>1.23E-21</td>\n",
       "      <td>1.09E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>1.50E-01</td>\n",
       "      <td>5.69E-21</td>\n",
       "      <td>6.13E-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281.5</th>\n",
       "      <td>2.12E-01</td>\n",
       "      <td>1.57E-19</td>\n",
       "      <td>2.75E-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3980</th>\n",
       "      <td>8.84E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "      <td>7.40E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3985</th>\n",
       "      <td>8.80E-03</td>\n",
       "      <td>7.43E-03</td>\n",
       "      <td>7.45E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3990</th>\n",
       "      <td>8.78E-03</td>\n",
       "      <td>7.37E-03</td>\n",
       "      <td>7.39E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>8.70E-03</td>\n",
       "      <td>7.21E-03</td>\n",
       "      <td>7.23E-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>8.68E-03</td>\n",
       "      <td>7.10E-03</td>\n",
       "      <td>7.12E-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2003 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      AM0                   AM1.5G  \\\n",
       "Wvlgth nm  Etr W*m-2*nm-1  Global tilt  W*m-2*nm-1   \n",
       "280              8.20E-02                 4.73E-23   \n",
       "280.5            9.90E-02                 1.23E-21   \n",
       "281              1.50E-01                 5.69E-21   \n",
       "281.5            2.12E-01                 1.57E-19   \n",
       "...                   ...                      ...   \n",
       "3980             8.84E-03                 7.39E-03   \n",
       "3985             8.80E-03                 7.43E-03   \n",
       "3990             8.78E-03                 7.37E-03   \n",
       "3995             8.70E-03                 7.21E-03   \n",
       "4000             8.68E-03                 7.10E-03   \n",
       "\n",
       "                                  AM1.5D  \n",
       "Wvlgth nm  Direct+circumsolar W*m-2*nm-1  \n",
       "280                             2.54E-26  \n",
       "280.5                           1.09E-24  \n",
       "281                             6.13E-24  \n",
       "281.5                           2.75E-22  \n",
       "...                                  ...  \n",
       "3980                            7.40E-03  \n",
       "3985                            7.45E-03  \n",
       "3990                            7.39E-03  \n",
       "3995                            7.23E-03  \n",
       "4000                            7.12E-03  \n",
       "\n",
       "[2003 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference = pd.read_csv('data/Reference_spectrum_ASTM-G173-03.csv', index_col=0, header=0) \n",
    "reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference.drop(reference.index[0], inplace=True)   # remove row including information on units\n",
    "reference=reference.astype(float)                  # convert values to float for easy operation\n",
    "reference.index=reference.index.astype(float)      # convert indexes to float for easy operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the data for the Xe-arc lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Xe arc lamp (W*m-2*nm-1)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavelength (nm)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300.36966</th>\n",
       "      <td>0.000793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.58005</th>\n",
       "      <td>0.000816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300.79044</th>\n",
       "      <td>0.000837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301.00082</th>\n",
       "      <td>0.000858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301.21124</th>\n",
       "      <td>0.000876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.77246</th>\n",
       "      <td>0.213920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.84302</th>\n",
       "      <td>0.213600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.91345</th>\n",
       "      <td>0.213280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199.98401</th>\n",
       "      <td>0.212960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200.05444</th>\n",
       "      <td>0.212640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6283 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Xe arc lamp (W*m-2*nm-1)\n",
       "wavelength (nm)                          \n",
       "300.36966                        0.000793\n",
       "300.58005                        0.000816\n",
       "300.79044                        0.000837\n",
       "301.00082                        0.000858\n",
       "301.21124                        0.000876\n",
       "...                                   ...\n",
       "1199.77246                       0.213920\n",
       "1199.84302                       0.213600\n",
       "1199.91345                       0.213280\n",
       "1199.98401                       0.212960\n",
       "1200.05444                       0.212640\n",
       "\n",
       "[6283 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xe_arc = pd.read_csv('data/Xe lamp spectral irradiance.csv', index_col=0, header=0) \n",
    "xe_arc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import the data for the quartz tungsten lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>W halogen lamp (W*m-2*nm-1)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wavelength (nm)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>0.000200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>0.000300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>0.654304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>0.653941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>0.651544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>0.647531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>0.644677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>901 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 W halogen lamp (W*m-2*nm-1)\n",
       "wavelength (nm)                             \n",
       "300                                 0.000001\n",
       "301                                 0.000010\n",
       "302                                 0.000100\n",
       "303                                 0.000200\n",
       "304                                 0.000300\n",
       "...                                      ...\n",
       "1196                                0.654304\n",
       "1197                                0.653941\n",
       "1198                                0.651544\n",
       "1199                                0.647531\n",
       "1200                                0.644677\n",
       "\n",
       "[901 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quartz_w = pd.read_csv('data/W lamp spectral irradiance.csv', index_col=0, header=0) \n",
    "quartz_w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the AM1.5G spectra, the Xe-arc lamp, and the quarz tungsten lamp spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28c83a83288>"
      ]
     },
     "execution_count": 6,
     "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(reference['AM1.5G'], \n",
    "         linewidth=2, label='AM1.5G')\n",
    "plt.plot(xe_arc, \n",
    "         linewidth=2, label='Xe-arc lamp')\n",
    "plt.plot(quartz_w, \n",
    "         linewidth=2, label='Quartz tungsten lamp')\n",
    "plt.ylabel('Spectral distribution (W*m-2*nm-1)')\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.xlim([250,1300])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the relevant constants and import the QE of the silicon solar cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "h=6.63*10**(-34)   # [J·s] Planck constant\n",
    "e=1.60*10**(-19)   # [C] electron charge\n",
    "c =299792458       #[m/s] Light speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>QE Silicon Solar cell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nm</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>0.185579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>0.243200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>0.298992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.353041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>0.405425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>0.081951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>0.067769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>0.053712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>0.039777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>0.025964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      QE Silicon Solar cell\n",
       "nm                         \n",
       "305                0.185579\n",
       "310                0.243200\n",
       "315                0.298992\n",
       "320                0.353041\n",
       "325                0.405425\n",
       "...                     ...\n",
       "1130               0.081951\n",
       "1135               0.067769\n",
       "1140               0.053712\n",
       "1145               0.039777\n",
       "1150               0.025964\n",
       "\n",
       "[170 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QE = pd.read_csv('data/QE_Silicon.csv', index_col=0, header=0) \n",
    "QE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the Spectral Response (SR) of the silicon solar cell and plot it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SR=pd.Series(index=QE.index,\n",
    "            data=[QE.loc[i,'QE Silicon Solar cell']*e*i*0.000000001/(h*c) for i in QE.index])\n",
    "plt.plot(SR, \n",
    "         linewidth=2)\n",
    "plt.ylabel('Spectral response, $SR$')\n",
    "plt.xlabel(r'Wavelength, $\\lambda$ (nm)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the reference AM1.5G, we interpolate the spectrum at those datapoints included in the SR, and integrate to obtain the short-circuit current density using Eq. 3.5.\n",
    "\n",
    "$J=\\int SR(\\lambda) \\cdot G(\\lambda) \\ d\\lambda$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photocurrent density = 36.7 mA/cm2\n"
     ]
    }
   ],
   "source": [
    "spectra=reference['AM1.5G']\n",
    "spectra_interpolated=np.interp(SR.index, spectra.index, spectra.values)\n",
    "\n",
    "J_reference = np.trapz([x*y for x,y in zip(SR, spectra_interpolated)], x=SR.index)*1000/10000 # A-> mA ; m2 -> cm2\n",
    "print('Photocurrent density = ' + str(J_reference.round(1)) + ' mA/cm2')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We repeat the calculation for the Xe-arc lamp spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photocurrent density = 27.1 mA/cm2\n"
     ]
    }
   ],
   "source": [
    "spectra=xe_arc['Xe arc lamp (W*m-2*nm-1)']\n",
    "spectra_interpolated=np.interp(SR.index, spectra.index, spectra.values)\n",
    "\n",
    "J_xe_arc = np.trapz([x*y for x,y in zip(SR, spectra_interpolated)], x=SR.index)*1000/10000 # A-> mA ; m2 -> cm2\n",
    "print('Photocurrent density = ' + str(J_xe_arc.round(1)) + ' mA/cm2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the error when determining the photocurrent density using the Xe-arc lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error = 26.0 %\n"
     ]
    }
   ],
   "source": [
    "error_xe_arc=1-J_xe_arc/J_reference\n",
    "print('Error = ' + str(100*error_xe_arc.round(2)) + ' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, the Xe-arc lamp should be moved 26 cm closer. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We repeat the calculation for the quartz tungsten lamp spectra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photocurrent density = 26.9 mA/cm2\n"
     ]
    }
   ],
   "source": [
    "spectra=quartz_w['W halogen lamp (W*m-2*nm-1)']\n",
    "spectra_interpolated=np.interp(SR.index, spectra.index, spectra.values)\n",
    "\n",
    "J_quartz_w = np.trapz([x*y for x,y in zip(SR, spectra_interpolated)], x=SR.index)*1000/10000 # A-> mA ; m2 -> cm2\n",
    "print('Photocurrent density = ' + str(J_quartz_w.round(1)) + ' mA/cm2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate the error when determining the photocurrent density using the quartz tungsten lamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error = 27.0 %\n"
     ]
    }
   ],
   "source": [
    "error_quartz_w=1-J_quartz_w/J_reference\n",
    "print('Error = ' + str(100*error_quartz_w.round(2)) + ' %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, the quartz tungsten lamp should be moved 27 cm closer. "
   ]
  }
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