Lab 5_Photoelectric Effect.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 5: The Photoelectric Effect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Physics 202\n",
    "\n",
    "Shreya Parjan + Alex G.\n",
    "\n",
    "10/15/18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats\n",
    "import scipy.optimize\n",
    "import math as m\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Planck's Constant and the Work Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#volts\n",
    "voltage630 = np.array([.19,.11,.12,.11,.12])\n",
    "voltage595 = np.array([.26,.25,.26,.29,.24])\n",
    "voltage525 = np.array([.56,.56,.59,.54,.54])\n",
    "voltage460 = np.array([.90,.82,.85,.89,.88])\n",
    "voltage400 = np.array([1.25,1.33,1.38,1.25,1.29])\n",
    "voltage380 = np.array([1.16,1.18,1.14,1.16,1.10])\n",
    "\n",
    "#m\n",
    "lled630 = 630e-9\n",
    "lled595 = 595e-9\n",
    "lled525 = 525e-9\n",
    "lled460 = 460e-9\n",
    "lled400 = 400e-9\n",
    "lled380 = 380e-9\n",
    "\n",
    "c = 3e8 #m/s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "avgV0 = np.array([np.average(voltage630), np.average(voltage595), np.average(voltage525), np.average(voltage460), np.average(voltage400), np.average(voltage380)])\n",
    "frequency = np.array([1/lled630, 1/lled595, 1/lled525, 1/lled460, 1/lled400, 1/lled380])*c\n",
    "#print(frequency)\n",
    "sigma = np.array([np.std(voltage630), np.std(voltage595), np.std(voltage525), np.std(voltage460), np.std(voltage400), np.std(voltage380)])\n",
    "#print(sigma)\n",
    "sigmaBar = np.std(avgV0)\n",
    "#print(sigmaBar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x-intercept =  426535294188251.06\n",
      "y-intercept =  -1.5357706726802571\n",
      "slope =  3.600571145239029e-15\n",
      "max lambda =  7.033415618534845e-07\n",
      "the max wavelength that will eject electrons is 703 nm\n",
      "uncertainties:  [2.48093320e-01 3.90758163e-16]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "plt.grid(True)\n",
    "plt.errorbar(frequency,avgV0,yerr=sigmaBar,fmt='o',markersize=1)\n",
    "def straight_line_model(x, A, B):\n",
    "    return A + B * x\n",
    "\n",
    "popt, pcov = scipy.optimize.curve_fit(straight_line_model, frequency, avgV0)\n",
    "yIntercept = popt[0]\n",
    "slope = popt[1]\n",
    "xIntercept = (-1)*yIntercept/slope\n",
    "maxLambda = c/xIntercept\n",
    "print('x-intercept = ',xIntercept)\n",
    "print('y-intercept = ',yIntercept)\n",
    "print('slope = ',slope)\n",
    "print('max lambda = ',maxLambda)\n",
    "print('the max wavelength that will eject electrons is 703 nm')\n",
    "\n",
    "frequency_for_plot = np.linspace(3e14, frequency.max(), 50)\n",
    "best_fit_line = straight_line_model(frequency_for_plot, popt[0], popt[1])\n",
    "\n",
    "#calculates uncertainties for both variables\n",
    "popt_uncertainties = np.sqrt(np.diag(pcov)) # get the diagonal elements then take sqrt\n",
    "print('uncertainties: ', popt_uncertainties)\n",
    "\n",
    "\n",
    "plt.annotate('Minimum frequency needed to knock an electron from the cathode', xy=(4.25e14,.02), xytext=(1e14,1), arrowprops={'arrowstyle': '-|>'}, va='center')\n",
    "\n",
    "plt.plot(frequency, avgV0, 'bo') #without '-' after bo, plots points alone\n",
    "plt.plot(frequency_for_plot, best_fit_line, 'b') # adds best fit line\n",
    "\n",
    "plt.xlabel('Frequency (Hz)') # label the x-axis\n",
    "plt.ylabel('Average Stopping Voltage (V)', size=12) # label the y-axis\n",
    "plt.ylim(0,1.75)\n",
    "plt.xlim(0,8e14)\n",
    "\n",
    "plt.title('Average Stopping Voltage vs. Frequency') # title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x-intercept =  2.88028168072499\n",
      "y-intercept =  0.8610526318661456\n",
      "slope =  -0.2989473694980457\n",
      "uncertainties:  [0.03191679 0.09480699]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filterV0 = np.array([.88,.88,.86,.84,.9])\n",
    "filterV3 = np.array([.76,.73,.73,.75,.75])\n",
    "filterV5 = np.array([.73,.73,.7,.73,.75])\n",
    "filters = np.array([0,.3,.5])\n",
    "\n",
    "avgV0 = np.mean(filterV0) #V\n",
    "avgV3 = np.mean(filterV3)\n",
    "avgV5 = np.mean(filterV5)\n",
    "avgV = np.array([avgV0,avgV3,avgV5])\n",
    "\n",
    "sigma = np.array([np.std(avgV0),np.std(avgV3),np.std(avgV5)])\n",
    "sigmaBar = np.std(avgV)\n",
    "\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "plt.grid(True)\n",
    "plt.errorbar(filters,avgV,yerr=sigmaBar,fmt='o',markersize=1)\n",
    "def straight_line_model(x, A, B):\n",
    "    return A + B * x\n",
    "\n",
    "popt, pcov = scipy.optimize.curve_fit(straight_line_model, filters, avgV)\n",
    "yIntercept = popt[0]\n",
    "slope = popt[1]\n",
    "xIntercept = (-1)*yIntercept/slope\n",
    "#maxLambda = c/xIntercept\n",
    "print('x-intercept = ',xIntercept)\n",
    "print('y-intercept = ',yIntercept)\n",
    "print('slope = ',slope)\n",
    "#print('the max wavelength that will eject electrons is',maxLambda,\" m\")\n",
    "\n",
    "filters_for_plot = np.linspace(3e14, filters.max(), 50)\n",
    "best_fit_line = straight_line_model(filters_for_plot, popt[0], popt[1])\n",
    "\n",
    "#calculates uncertainties for both variables\n",
    "popt_uncertainties = np.sqrt(np.diag(pcov)) # get the diagonal elements then take sqrt\n",
    "print('uncertainties: ', popt_uncertainties)\n",
    "\n",
    "\n",
    "#plt.annotate('Minimum frequency needed to knock an electron from the cathode', xy=(4.25e14,.02), xytext=(1e14,1), arrowprops={'arrowstyle': '-|>'}, va='center')\n",
    "\n",
    "plt.plot(filters, avgV, 'bo') #without '-' after bo, plots points alone\n",
    "#plt.plot(filters_for_plot, best_fit_line, 'b') # adds best fit line\n",
    "\n",
    "plt.xlabel('Filter') # label the x-axis\n",
    "plt.ylabel('Average Stopping Voltage (V)', size=12) # label the y-axis\n",
    "plt.ylim(0,.95)\n",
    "#plt.xlim(0,8e14)\n",
    "\n",
    "plt.title('Average Stopping Voltage vs. Intensity') # title\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Photocurrent Voltage v. Filter')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "photo = np.array([14.77, 8.49,6.47]) #mV\n",
    "\n",
    "plt.plot(filters, photo, '8', label='Filters v. Photocurrent')\n",
    "popt, pcov = scipy.optimize.curve_fit(straight_line_model, filters, photo)\n",
    "plt.xlabel('Filter #')\n",
    "plt.ylabel('Photocurrent Voltage (mV)')\n",
    "plt.title('Photocurrent Voltage v. Filter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions\n",
    "1. Our value for h (Planck's constant) is: h = (3.6 +/- 0.4)*10^-15 eV*s.\n",
    "2. This does not agree with the accepted value for Planck's constant (h = 4.136 x 10-15 eV*s).\n",
    "3. The max wavelength that will eject electrons is about 700 nm.\n",
    "4. Our data suggests that as intensity increases, there is little change in the stopping voltage while classically, V0 should be proportional to intensity.\n",
    "5. Photocurrent seems approximately proportional to intensity.\n",
    "6. Classically, we'd expect the stopping voltage and photocurrent to depend on the intensity of light but our results indicated that there is little change in the stopping voltage with increasing intensity. We would also not expect there to be a maximum wavelength that ejects an electron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}