{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73a0989c",
   "metadata": {},
   "source": [
    "# Compare the Different EQCM Runs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb5a4ffb",
   "metadata": {},
   "source": [
    "## Cluster the EQCM-D Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fedac82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    time (s)  cycle  \\\n",
      "fname                                                                 \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.00    1.0   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.02    1.0   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.04    1.0   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.06    1.0   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.08    1.0   \n",
      "\n",
      "                                                    potential (V)  \\\n",
      "fname                                                               \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.099803   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.099905   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.100007   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.100108   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.100210   \n",
      "\n",
      "                                                    current (mA)  \\\n",
      "fname                                                              \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.002746   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.002655   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.002565   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.002475   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...      0.002385   \n",
      "\n",
      "                                                    current density (mAcm-2)  \\\n",
      "fname                                                                          \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...                  0.002347   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...                  0.002270   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...                  0.002193   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...                  0.002116   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...                  0.002039   \n",
      "\n",
      "                                                    df/n 3 (Hz)  dG/n 3 (Hz)  \\\n",
      "fname                                                                          \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -1.906351     2.542292   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -1.906536     2.541483   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -1.905007     2.538381   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -1.902723     2.534639   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -1.902000     2.537682   \n",
      "\n",
      "                                                    df/n 5 (Hz)  dG/n 5 (Hz)  \\\n",
      "fname                                                                          \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -2.057040     0.739803   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -2.057205     0.739930   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -2.056916     0.738889   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -2.061665     0.735241   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -2.064993     0.731075   \n",
      "\n",
      "                                                    df/n 7 (Hz)  dG/n 7 (Hz)  \\\n",
      "fname                                                                          \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -3.010551     0.332096   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -3.011909     0.337000   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -3.014240     0.333835   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -3.014393     0.333929   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -3.022140     0.339123   \n",
      "\n",
      "                                                    df/n 9 (Hz)  dG/n 9 (Hz)  \n",
      "fname                                                                         \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -5.426320     0.586707  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -5.427302     0.585203  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -5.427994     0.585251  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -5.428288     0.584717  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    -5.428125     0.583885  \n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "folder = \"/Users/leppin/Downloads/Research_ Leppin_2025 _Interfacial Softening and Electrolyte Uptake in Co3O4 OER Catalysts/Data/FastEQCM-D/\"  # root directory\n",
    "AllEQCMData = pd.DataFrame()\n",
    "\n",
    "# loop over folders containing 'SmallLoadingRepeat'\n",
    "for root, dirs, files in os.walk(folder):\n",
    "    if \"SmallLoadingRepeat\" in os.path.basename(root):\n",
    "        parquet_files = [f for f in files if f.endswith(\"AllEQCMData_indexed.parquet\")]\n",
    "        for pq_file in parquet_files:\n",
    "            pq_path = os.path.join(root, pq_file)\n",
    "            EQCMData = pd.read_parquet(pq_path)  # still reading as pickle\n",
    "            AllEQCMData = pd.concat([AllEQCMData, EQCMData])\n",
    "\n",
    "print(AllEQCMData.head())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556ffbb3",
   "metadata": {},
   "source": [
    "## Select Cycle 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac87c486",
   "metadata": {},
   "outputs": [],
   "source": [
    "SelectedData = AllEQCMData.loc[AllEQCMData['cycle']== 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c81477f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:133: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:165: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:133: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:165: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_22661/2581238369.py:133: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axs[0,1].set_ylabel('$\\mathrm{mass_{geo}}$ \\n ($\\mathrm{\\mu g \\ cm^{-2}}$)')\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_22661/2581238369.py:165: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axs[1,0].set_ylabel('$d/dt(\\mathrm{mass_{geo}})$ \\n ($\\mathrm{ ng \\ cm^{-2} \\ s^{-1}}$)')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr\n",
      "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr\n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr\n",
      "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr\n",
      "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr\n",
      "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x250 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "from matplotlib import cm\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "A = 1.17 #cm2\n",
    "#AllEQCMData['current density (mAcm-2)'] = AllEQCMData['current (mA)']/A\n",
    "\n",
    "\n",
    "def convert_potential(E_old, pH=13, Eref_old = 0.1635): \n",
    "    E_new = E_old + Eref_old + 0.059*pH\n",
    "    return E_new\n",
    "\n",
    "\n",
    "n_cycle = 3\n",
    "\n",
    "def CalcSauerbreyMass(df_by_n: np.ndarray, Filter: bool = True) -> np.ndarray: \n",
    "    Zq = 8.8e6\n",
    "    f0 = 5e6\n",
    "    MassDensity = -df_by_n * Zq / (2*f0**2) * 1e5  # conversion of kg/m2 in ug/cm2\n",
    "    return gaussian_filter1d(MassDensity, 150, axis=0) if Filter else MassDensity\n",
    "\n",
    "def discard_points(y: np.ndarray, trsh: int) -> np.ndarray: \n",
    "    y[np.abs(y) > trsh] = np.nan\n",
    "    return pd.Series(y).interpolate().to_numpy()\n",
    "\n",
    "def colors(i):\n",
    "    colors = cm.YlGnBu_r((i)*90)#cm.PuRd_r((i)*90)\n",
    "    return colors\n",
    "\n",
    "def make_color_label_line(df: pd.DataFrame) -> tuple[str, str, str]: \n",
    "    if set(df['info']) == {'pristine'}:\n",
    "        color = colors(0)\n",
    "        label = 'Pristine'\n",
    "    elif set(df['info']) == {'0.8 V'}:\n",
    "        color = colors(1) \n",
    "        label = '1 h at 1.73 V'\n",
    "    elif set(df['info']) == {'1.0 V'}: \n",
    "        color = colors(2)\n",
    "        label = '1 h at 1.93 V'\n",
    "\n",
    "    if set(df['sample']) == {'#6'}:\n",
    "        line = '--'\n",
    "    elif set(df['sample']) == {'#8'}:\n",
    "        line = '-.' \n",
    "    elif set(df['sample']) == {'#11'}:\n",
    "        line = ':'\n",
    "    elif set(df['sample']) == {'#18'}:\n",
    "        line = '-'\n",
    "    return color, label, line\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "SelectedData = AllEQCMData[AllEQCMData['cycle'].isin([n_cycle])]\n",
    "\n",
    "SelectedData = SelectedData.copy()\n",
    "SelectedData['info'] = ''\n",
    "SelectedData['sample'] = ''\n",
    "\n",
    "\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('at_0'),'info'] = '0.8 V'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('at_1'),'info'] = '1.0 V'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('init'),'info'] = 'pristine'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('#6'),'sample'] = '#6'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('#8'),'sample'] = '#8'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('#11'),'sample'] = '#11'\n",
    "\n",
    "SelectedData.loc[SelectedData.index.get_level_values('fname').str.contains('#18'),'sample'] = '#18'\n",
    "\n",
    "def sample_sort_key(s):\n",
    "    if \"initial\" in s:\n",
    "        return 0\n",
    "    elif \"at_0\" in s:\n",
    "        return 1\n",
    "    elif \"at_1\" in s or \"at_1V\" in s:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3  # fallback\n",
    "\n",
    "samples = sorted(set(SelectedData.index.get_level_values('fname')))\n",
    "samples = sorted(samples, key=sample_sort_key)\n",
    "\n",
    "# Legend\n",
    "plt.rcParams['legend.fontsize'] = 7\n",
    "plt.rcParams['legend.frameon'] = False\n",
    "# Axes\n",
    "plt.rcParams['axes.labelsize'] = 7\n",
    "plt.rcParams['axes.titlesize'] = 7\n",
    "# Ticks\n",
    "plt.rcParams['xtick.labelsize'] = 6\n",
    "plt.rcParams['ytick.labelsize'] = 6\n",
    "fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, figsize=[3.5,2.5], constrained_layout = True)\n",
    "axs[0,0].tick_params(which='both', direction=\"in\")\n",
    "def colors(i):\n",
    "    colors = cm.YlGnBu_r((i)*90)#cm.PuRd_r((i)*90)\n",
    "    return colors\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    print(sample)\n",
    "    df = SelectedData.loc[(sample)]\n",
    "    color, label, line = make_color_label_line(df)\n",
    "    len_anodic_wave = len(df['df/n 3 (Hz)']) // 2\n",
    "\n",
    "    # Plot current\n",
    "\n",
    "    axs[0,0].plot(convert_potential(df['potential (V)'].iloc[:len_anodic_wave]), df['current density (mAcm-2)'].iloc[:len_anodic_wave],\n",
    "                    linestyle=line, color=color, alpha = 1, label=f'{label} repeat {i_sample%4 +1}')\n",
    "    axs[0,0].plot(convert_potential(df['potential (V)'].iloc[len_anodic_wave:]), df['current density (mAcm-2)'].iloc[len_anodic_wave:],\n",
    "                        linestyle=line, color=color, alpha = 1)\n",
    "axs[0,0].set_ylim(top = 0.3) \n",
    "axs[0,0].set_ylabel(r'$i_{\\mathrm{geo}} \\ \\mathrm{(mA \\ cm^{-2}})$', fontsize=6)\n",
    "axs[0,0].set_ylabel(r'$i_{\\mathrm{geo}} \\ \\mathrm{(mA \\ cm^{-2}})$')\n",
    "\n",
    "ovt_keys_f = [SelectedData.keys()[5::2][0]]\n",
    "axs[0,1].tick_params(which='both', direction=\"in\", labelsize=6)\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    df = SelectedData.loc[(sample)]\n",
    "    color, label, line = make_color_label_line(df)\n",
    "    SauerbreyMass = CalcSauerbreyMass(df['df/n 3 (Hz)'] - df['df/n 3 (Hz)'].iloc[0])\n",
    "    axs[0,1].plot(convert_potential(df['potential (V)'].iloc[:len_anodic_wave]), SauerbreyMass[:len_anodic_wave],\n",
    "                    linestyle=line, color=color, alpha = 1)\n",
    "    axs[0,1].plot(convert_potential(df['potential (V)'].iloc[len_anodic_wave:]), SauerbreyMass[len_anodic_wave:],\n",
    "                    linestyle=line, color=color, alpha = 1)\n",
    "axs[0,1].set_ylabel('$\\mathrm{mass_{geo}}$ \\n ($\\mathrm{\\mu g \\ cm^{-2}}$)')\n",
    "axs[0,1].tick_params(which='both', direction = 'in')\n",
    "\n",
    "\n",
    "\n",
    "ovt_keys_g = [SelectedData.keys()[6::2][0]]\n",
    "axs[1,1].tick_params(which='both', direction=\"in\")\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    df = SelectedData.loc[(sample)]\n",
    "    color, label, line = make_color_label_line(df)\n",
    "    for i_ovt, ovt_key in enumerate(ovt_keys_g):\n",
    "        axs[1,1].plot(convert_potential(df['potential (V)'].iloc[:len_anodic_wave]), (df[ovt_key] - df[ovt_key].iloc[0]).iloc[:len_anodic_wave],\n",
    "                            linestyle=line, color=color, alpha = 1)\n",
    "        axs[1,1].plot(convert_potential(df['potential (V)'].iloc[len_anodic_wave:]), (df[ovt_key] - df[ovt_key].iloc[0]).iloc[len_anodic_wave:],\n",
    "                           linestyle=line, color=color, alpha = 1)\n",
    "\n",
    "axs[1,1].set_ylabel(r'$(\\Delta \\Gamma/n)_{15 \\ \\mathrm{MHz}}$ (Hz)')\n",
    "axs[1,1].set_xlabel('Potential vs RHE (V)')\n",
    "axs[1,0].set_xlabel('Potential vs RHE (V)')\n",
    "\n",
    "axs[1,0].tick_params(which='both', direction=\"in\")\n",
    "for i_sample, sample in enumerate(samples):\n",
    "    df = SelectedData.loc[(sample)]\n",
    "    color, label, line = make_color_label_line(df)\n",
    "    SauerbreyMass = CalcSauerbreyMass(df['df/n 3 (Hz)'] - df['df/n 3 (Hz)'].iloc[0])\n",
    "    time = df['time (s)'].to_numpy()\n",
    "    rate = (np.roll(SauerbreyMass, -1) - SauerbreyMass) / (time[1] - time[0])*1000\n",
    "    rate = rate[5:-5]\n",
    "    axs[1,0].plot(convert_potential(df['potential (V)'].iloc[5:-5].iloc[:len_anodic_wave]), rate[:len_anodic_wave],\n",
    "                       linestyle=line, color=color, alpha = 1)\n",
    "    axs[1,0].plot(convert_potential(df['potential (V)'].iloc[5:-5].iloc[len_anodic_wave:]), rate[len_anodic_wave:],\n",
    "                       linestyle=line, color=color, alpha = 1)\n",
    "axs[1,0].set_ylabel('$d/dt(\\mathrm{mass_{geo}})$ \\n ($\\mathrm{ ng \\ cm^{-2} \\ s^{-1}}$)')\n",
    "\n",
    "for ax in np.ravel(axs):\n",
    "    ax.margins(x=0.1, y=0.1)\n",
    "    ax.set_xticks([1.0, 1.2, 1.4, 1.6])\n",
    "    ax.yaxis.set_minor_locator(AutoMinorLocator(n = 2))\n",
    "    ax.xaxis.set_minor_locator(AutoMinorLocator(n = 4))\n",
    "fig.legend(frameon = False, handlelength=3, loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "plt.savefig(fname=f\"all_result_{n_cycle}_comparison_SmallLoading.tif\", dpi=600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight', transparent = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6574fb",
   "metadata": {},
   "source": [
    "## Cluster all Tafel Slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa578d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R3_tafel_plot_data_all.parquet\n",
      "tafel_plot_data_all.parquet\n",
      "4tafel_plot_data_all.parquet\n",
      "R4tafel_plot_data_all.parquet\n",
      "R2_tafel_plot_data_all.parquet\n",
      "tafel_plot_data_all.parquet\n",
      "tafel_plot_data_all.parquet\n",
      "R1_tafel_plot_data_all.parquet\n",
      "                                                    tafel slope     error  \\\n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.774025  0.703758   \n",
      "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    83.734148  0.580130   \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    89.024878  0.594212   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.497274  0.235142   \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    91.056685  0.242149   \n",
      "\n",
      "                                                    overpotential  \\\n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.742386   \n",
      "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739678   \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.740460   \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.732842   \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.736605   \n",
      "\n",
      "                                                             info before EIS  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No  \n",
      "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V         No  \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V         No  \n",
      "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes  \n",
      "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V        Yes  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr',\n",
       " 'CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr',\n",
       " 'CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr',\n",
       " 'CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr',\n",
       " 'CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq5_09_CV_C01.mpr',\n",
       " 'CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr',\n",
       " 'CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr',\n",
       " 'CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr',\n",
       " 'CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq5_09_CV_C01.mpr',\n",
       " 'CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr',\n",
       " 'CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr',\n",
       " 'CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq5_09_CV_C01.mpr']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "folder = \"/Users/leppin/Documents/SYNC/People/Christian/Electroresponsivity_Co3O4/Folder_For_DataBase/FastEQCM-D/\"  # root directory\n",
    "AllTafelData = pd.DataFrame()\n",
    "\n",
    "# loop over folders containing 'SmallLoadingRepeat'\n",
    "for root, dirs, files in os.walk(folder):\n",
    "    if \"SmallLoadingRepeat\" in os.path.basename(root):\n",
    "        parquet_files = [f for f in files if f.endswith(\"_all.parquet\")]\n",
    "        for pq_file in parquet_files:\n",
    "            print(pq_file)\n",
    "            pq_path = os.path.join(root, pq_file)\n",
    "            TafelData = pd.read_parquet(pq_path)\n",
    "            AllTafelData = pd.concat([AllTafelData, TafelData])\n",
    "\n",
    "print(AllTafelData.head())\n",
    "\n",
    "# Add info and sample columns\n",
    "AllTafelData['info'] = ''\n",
    "AllTafelData['sample'] = ''\n",
    "\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('at_0'), 'info'] = '1 h at 1.73 V'\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('at_1'), 'info'] = '1 h at 1.93 V'\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('initial'), 'info'] = 'Pristine'\n",
    "\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('#6'), 'sample'] = '#6'\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('#8'), 'sample'] = '#8'\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('#11'), 'sample'] = '#11'\n",
    "AllTafelData.loc[AllTafelData.index.str.contains('#18'), 'sample'] = '#18'\n",
    "\n",
    "# Sorting helper\n",
    "def sample_sort_key(s):\n",
    "    if \"initial\" in s:\n",
    "        return 0\n",
    "    elif \"at_0\" in s:\n",
    "        return 1\n",
    "    elif \"at_1\" in s or \"at_1V\" in s:\n",
    "        return 2\n",
    "    else:\n",
    "        return 3  # fallback\n",
    "\n",
    "samples = sorted(set(AllTafelData.index), key=sample_sort_key)\n",
    "samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "16ab2222",
   "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>tafel slope</th>\n",
       "      <th>error</th>\n",
       "      <th>overpotential</th>\n",
       "      <th>info</th>\n",
       "      <th>before EIS</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.774025</td>\n",
       "      <td>0.703758</td>\n",
       "      <td>0.742386</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>83.734148</td>\n",
       "      <td>0.580130</td>\n",
       "      <td>0.739678</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>89.024878</td>\n",
       "      <td>0.594212</td>\n",
       "      <td>0.740460</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>80.497274</td>\n",
       "      <td>0.235142</td>\n",
       "      <td>0.732842</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>91.056685</td>\n",
       "      <td>0.242149</td>\n",
       "      <td>0.736605</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>94.454034</td>\n",
       "      <td>0.302221</td>\n",
       "      <td>0.739996</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.774025</td>\n",
       "      <td>0.703758</td>\n",
       "      <td>0.742386</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>83.734148</td>\n",
       "      <td>0.580130</td>\n",
       "      <td>0.739678</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>89.024878</td>\n",
       "      <td>0.594212</td>\n",
       "      <td>0.740460</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>80.497274</td>\n",
       "      <td>0.235142</td>\n",
       "      <td>0.732842</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>91.056685</td>\n",
       "      <td>0.242149</td>\n",
       "      <td>0.736605</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>94.454034</td>\n",
       "      <td>0.302221</td>\n",
       "      <td>0.739996</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq5_09_CV_C01.mpr</th>\n",
       "      <td>75.892803</td>\n",
       "      <td>0.128195</td>\n",
       "      <td>0.714630</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>77.179163</td>\n",
       "      <td>0.125835</td>\n",
       "      <td>0.706690</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>85.505755</td>\n",
       "      <td>0.084597</td>\n",
       "      <td>0.709119</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>77.535165</td>\n",
       "      <td>0.026083</td>\n",
       "      <td>0.719295</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>79.021256</td>\n",
       "      <td>0.032431</td>\n",
       "      <td>0.707082</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>88.302871</td>\n",
       "      <td>0.035054</td>\n",
       "      <td>0.702502</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq5_09_CV_C01.mpr</th>\n",
       "      <td>75.892803</td>\n",
       "      <td>0.128195</td>\n",
       "      <td>0.714630</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>77.179163</td>\n",
       "      <td>0.125835</td>\n",
       "      <td>0.706690</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>85.505755</td>\n",
       "      <td>0.084597</td>\n",
       "      <td>0.709119</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>77.535165</td>\n",
       "      <td>0.026083</td>\n",
       "      <td>0.719295</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>79.021256</td>\n",
       "      <td>0.032431</td>\n",
       "      <td>0.707082</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>88.302871</td>\n",
       "      <td>0.035054</td>\n",
       "      <td>0.702502</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>69.355261</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.731392</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.215607</td>\n",
       "      <td>0.227517</td>\n",
       "      <td>0.724418</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>92.690114</td>\n",
       "      <td>0.256467</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>81.298775</td>\n",
       "      <td>0.152826</td>\n",
       "      <td>0.721658</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>85.339898</td>\n",
       "      <td>0.208972</td>\n",
       "      <td>0.741697</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>97.999830</td>\n",
       "      <td>0.096858</td>\n",
       "      <td>0.721691</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>69.355261</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.731392</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.215607</td>\n",
       "      <td>0.227517</td>\n",
       "      <td>0.724418</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>92.690114</td>\n",
       "      <td>0.256467</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>81.298775</td>\n",
       "      <td>0.152826</td>\n",
       "      <td>0.721658</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>85.339898</td>\n",
       "      <td>0.208972</td>\n",
       "      <td>0.741697</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>97.999830</td>\n",
       "      <td>0.096858</td>\n",
       "      <td>0.721691</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>67.333172</td>\n",
       "      <td>0.054941</td>\n",
       "      <td>0.723507</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>75.266025</td>\n",
       "      <td>0.135410</td>\n",
       "      <td>0.724447</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>86.451327</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>0.710156</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>70.967225</td>\n",
       "      <td>0.064506</td>\n",
       "      <td>0.727310</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>72.850854</td>\n",
       "      <td>0.123063</td>\n",
       "      <td>0.718928</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>76.271633</td>\n",
       "      <td>0.093756</td>\n",
       "      <td>0.725029</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>67.333172</td>\n",
       "      <td>0.054941</td>\n",
       "      <td>0.723507</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>75.266025</td>\n",
       "      <td>0.135410</td>\n",
       "      <td>0.724447</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>86.451327</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>0.710156</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>70.967225</td>\n",
       "      <td>0.064506</td>\n",
       "      <td>0.727310</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>72.850854</td>\n",
       "      <td>0.123063</td>\n",
       "      <td>0.718928</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>76.271633</td>\n",
       "      <td>0.093756</td>\n",
       "      <td>0.725029</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    tafel slope     error  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.774025  0.703758   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    83.734148  0.580130   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    89.024878  0.594212   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.497274  0.235142   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    91.056685  0.242149   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    94.454034  0.302221   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.774025  0.703758   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    83.734148  0.580130   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    89.024878  0.594212   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.497274  0.235142   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    91.056685  0.242149   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    94.454034  0.302221   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    75.892803  0.128195   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    77.179163  0.125835   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    85.505755  0.084597   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    77.535165  0.026083   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    79.021256  0.032431   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    88.302871  0.035054   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    75.892803  0.128195   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    77.179163  0.125835   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    85.505755  0.084597   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    77.535165  0.026083   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    79.021256  0.032431   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    88.302871  0.035054   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    69.355261  0.185000   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...    80.215607  0.227517   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    92.690114  0.256467   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...    81.298775  0.152826   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    85.339898  0.208972   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    97.999830  0.096858   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    69.355261  0.185000   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...    80.215607  0.227517   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    92.690114  0.256467   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...    81.298775  0.152826   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    85.339898  0.208972   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    97.999830  0.096858   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    67.333172  0.054941   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...    75.266025  0.135410   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...    86.451327  0.143573   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    70.967225  0.064506   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...    72.850854  0.123063   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...    76.271633  0.093756   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    67.333172  0.054941   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...    75.266025  0.135410   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...    86.451327  0.143573   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    70.967225  0.064506   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...    72.850854  0.123063   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...    76.271633  0.093756   \n",
       "\n",
       "                                                    overpotential  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.742386   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739678   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.740460   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.732842   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.736605   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739996   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.742386   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739678   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.740460   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.732842   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.736605   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739996   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.714630   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.706690   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.709119   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.719295   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.707082   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.702502   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.714630   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.706690   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.709119   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.719295   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.707082   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.702502   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.731392   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.724418   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.733577   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721658   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.741697   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721691   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.731392   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.724418   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.733577   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721658   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.741697   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721691   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.723507   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...       0.724447   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.710156   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.727310   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.718928   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.725029   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.723507   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...       0.724447   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.710156   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.727310   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.718928   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.725029   \n",
       "\n",
       "                                                             info before EIS  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V         No   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V         No   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V        Yes   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V        Yes   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V         No   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V         No   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V        Yes   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V        Yes   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V         No   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V        Yes   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V        Yes   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V         No   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V        Yes   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V        Yes   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V         No   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V         No   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V        Yes   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V        Yes   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V         No   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V         No   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V        Yes   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...  1 h at 1.73 V         No   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V         No   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.73 V        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...  1 h at 1.73 V         No   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V         No   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.73 V        Yes   \n",
       "\n",
       "                                                   sample  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...     #6  \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...     #6  \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllTafelData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "554bfc6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:97: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "<>:97: SyntaxWarning: invalid escape sequence '\\m'\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_22661/3126386599.py:13: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axs.set_ylabel('Tafel slope \\n $\\mathrm{(mV \\ dec^{-1})}$', fontsize = 7)\n",
      "/var/folders/f2/krn3py8536556jbm969hx5380000gn/T/ipykernel_22661/3126386599.py:97: SyntaxWarning: invalid escape sequence '\\m'\n",
      "  axs.set_ylabel('Overpotential vs RHE \\n at 0.1 $\\mathrm{mA \\ cm^{-2} \\ (V)}$', fontsize = 7)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Oq1evRmRkJLKzs+Hg4IC5c+cqZXVuDUop0yMlJ1M1x4IFCzB58mQUFxcjPDwcBw4cMLhFDgEAw4YB+/ZhFo+HlGfPkPT770iquzQcwEweDxCLpfcxdEabNm2aNX9rA7X3tBUKhXj8+DG6deumdJhbt27hzJkzkEgkGDJkSKMWlxeF2nvapqUBfn4t35eaChjI+AyjebSyp+3YsWNRUVGBsrIyuLm5Yfjw4QrrPDTGiRMn5Me9e/dgb28PBwcH5OTk4MSJE6okrxW8vb3h6uqqvAnd11eqKG+/DTRspnK50vNMkYyCbdu2wdXVFd5KOsaqVDP169cPGRkZ+OGHH3Dv3j2sXr0ab7zxBq42M91A5pVQWFiItLQ0DBkyBESExMRE+Pr64vjx48omr1E0stt6TQ0QGQlkZwMODsDcuYCW2+WMF4+yZUWlQVuhUIjs7Gzs27cPERERSs1T2l23G8HQoUNx8+ZNvFbXMS8oKMC0adNUSV7/aNMGeMHtcob+olIzb+XKlRg7dix69+4NHx8f3Lt377nVipri4cOHClPcO3fujIcPH6omLYOhx6htgFCVr7/+GnFxcRg7diw4HA4OHz6MESNGICws7EUk/xwaaeYxXgqULSsvTJkA4NKlS0hJSQEADBo0CF5eXi8q6edgysRQFq30mVpL//790b9/f43GmZOTA29vb7nTbGxsLJKSkp7bfZ3B0DYGudlZQ/h8Pg4ePAhAuhLSt99+i3PnzuHixYsIDw9HdHR0CzEwGK3nhbkTaZOUlBQEBAQgICAA06dPb3T3dQZD22jdnSgyMhITJkzAq3V752iarl274u7du2jXrh0+/PBDHDp0qNHd15ui4bb06u5nyjAehEIhhEKh/HfDMtIk6njRFhYWKn3v/PnzqUePHjRkyBD6/vvv6fHjx+okqRTHjx+niRMn0qxZs+TnBg4c2Oi96q4BwTB+1F0DQqVxpjNnzsDFxQUDBgwAAFy5cgUzZsxoNszmzZvx999/Y8WKFcjMzMQbb7yBkSNHYs+ePSjTwEqhFRUV8u/Jycl4++235buvp6amyndfbwqBQICysjL5ERoa2mqZGIZNaGioQpkQCATKBVRFY/v370/FxcUKc5L69OmjktaLxWJKSEigN954g8zMzFQK2xgnTpwgT09PGjRoEE2fPp1qa2vpl19+IV9fXwoKCqK///670XBsg2iGsmhlDQgulytf+UcGj8dTOnxycjJiY2Nx5MgRuLm5KawAoy4jR47EyJEjFc5NnjwZkydPbnXcDIYqqKRMrq6uOHToEIgIAoEAmzZtkjf5muL8+fM4cOAAjhw5AhcXF0yaNAmrVq3CK6+80irBGQx9Q6U+07Zt25Ceng5TU1OMHTsWbdq0waZNm5oNs2LFCri5uSE9PR3x8fGYPXs2UySGUaKUO9H48ePx66+/YvXq1Vi+fPmLkEvrMHcihrJo1J3o5s2buHz5Mvbv34/x48c/tyuCMjNms7OzsWXLFjx48EBh7Ofo0aPKiMBg6D1KKdM333yDr776CgKB4DlvBw6Hg7Nnz7YYx9ixYzFv3jxMnjzZIBdSYTBaRBUT4Zo1a9Q2L/r4+KgdVhvIzJ1OTk7k4uJCW7du1bVIDD1j69at5OLiQk5OTkqZxlWegpGWloasrCwFdwtlfPOOHDmC1NRUDB06VMFdZ7CW9hdtCdZnYiiLVqZgLF68GA8ePEBqairef/99HD58GL6+vkopU3x8PJKSknD37l15M4/D4ehMmRgMjaNKtefu7k5E/1uJtbS0lPh8vlJhHR0dSSKRqJKcVmEeEAxlUbasKGUJGDRoEACgbdu28s+ioiK0a9cOubm5SintgAEDkJ2drZbCMxiGgFLNvKq63bZHjRqF0tJSLFmyBJ6enuByuZg5c6ZSCV2/fh1ubm5wdnaGmZkZiAgcDgcXLlxQX3oGQ49QygBha2v7nDc1EaG0tBSvvPKKUn2mBw8eNHq+Z8+eSoqqWZgBgqEsGl3RVSQSobi4GI8ePZIfxcXF8vPNMWzYMOzcuRPt2rVDz5495YetrS3y8vIwd+5c7NmzR6XM1efChQvw9fXF4MGD8d5776G2thaOjo4IDAxEYGAgTp8+rXbcDIYqKNXM69q1a4vLIDfF4cOHsXPnTgQHB6OoqAivvPIKnj17hurqavD5fISEhMDHx0etuAFprXn27FmYm5sjNDQUR44cgZWVFZKSktSOk8FQB6WUSYmWYJO0b98eCxcuxMKFC1FTU4PHjx+jbdu2GnN27dq1q/x7mzZtwOVyUVlZCT6fj+7du2Pr1q3o1KlTk+HZtHVGQ7Q6bb2mpqa11kWtk5OTQwMHDqSamhoqLi4mIqK9e/c2uRkwm7bOaAp1p62/0EUotUV5eTmCg4OxY8cOODs7y89XVVVhxIgR+P333xsNY2VlBYFAoNCpZDUTo7GaydbWVr8WodQGIpEIU6ZMQVhYGJydnVFTUwMigpmZGZKTk1tcC93S0pJZ8xgKqPtCNXhl+vnnn/Hnn38iPDwc4eHhCAkJwbp169C+fXuYmZlh165duhaR8ZJgFM08dWDjTAxl0crOgQwGo2mYMjEYGoIpE4OhIZgyMRgagikTg6EhXnpl8vb2hqurK7Zt26ZrURh6xrZt2+Dq6gpvb2+l7memcWYaZ7QAM40zGC8YpkwMhoZgytRKhEIhvv76awXHSEPFmPICvPj8sD5TK/tMxtT3Mqa8AC/+Pzbqmmnp0qXyTaNra2t1LQ7DyDFaZbpy5Qpyc3ORnJyM3r174+DBg7oWiWHkGPwUjKZITU3F8OHDAQAjRozA7t278d5778mvy1q3ubm5CtOSVZ3LIgur9NRmPcaY8gKon5+GkwNl+ya31CMyWmV68uSJfH0IKysrlJSUKFyXPSBltsNRBltbW43Eow8YU14AzeWnoqICVlZWTV43WmXq2LGj/I1UVlb23KIq3bp1Q3Z2NkxNTcHhcOTn2bR1RsOaiYhQW1uLbt26NRvOaJXJz88P3377LWbMmIH4+Hj4+/srXOdyuejVq5eOpGMYI0ZrgOjbty9sbGwQEBCAGzduYPz48boWiWHkvLTjTAyGpjHamonRNJGRkRCJRLoWQ2McOnQIEolE12IwZXrZyM/Px+3bt5GXlwegdav16gMZGRlISUnRtRgAmDK1SP2d4WXow1tQXWTLUv/8888AoGDJNEQcHR3x4MEDbN68WdeiMGVqDiICj8eDQCDA2rVrsX79epSWlhrcbvFEhPv376OkpARt27ZFaGgoEhMTcfbsWV2LphZEhKqqKtTW1sLCwgLr16/HhQsXkJWVpVO5DKtUvGA4HA7Ky8sxbdo09O3bF3/99RemTp0qr5kMpYl04MABrF27FsuXL8fZs2dhY2ODDz74AEVFRQAar331me3bt2P69OnYuHEj0tLSYGdnB3d3dxQWFgLQYctBGwufGzr19969fv06LV68mIqKiujtt9+mP/74g+7evUvV1dU6lFB18vLy6LfffqNBgwbRrl27aNGiRTRmzBjKz8/XtWhqcenSJfrll1+of//+dOzYMfryyy9p+PDh8k0bdAGrmRogkUjA4XDkW4+6uLjg8ePHcHNzw4YNGwAA//rXv55zT9J3XnvtNQQHByM6OhpWVlaQSCQ4efIkoqKiDLIP6OXlhcmTJ2Pr1q24desWACAlJQXHjh3TWYvBaD0g1EEsFoPH46GgoACTJk1CQEAAXF1dMW/ePADA7t27kZKSgujoaIV9ofQNiUSCDRs2oHv37vD29oajoyMAabPU1dUVrq6uGDduHOzt7TFu3Di97wNKJBJERUXBxcUFzs7OcrceIoKPjw88PT1RW1sLoVCIoKAgnRlV2KBtA0pLS/HZZ58hMDAQr7/+Os6dO4fu3bvjvffew8OHD2FhYQEbGxtdi9kkEokE06ZNQ/v27eHt7Y1Hjx5h2bJl4PF4kEgk4HK58peGISDLD5fLhYeHB2xtbeXe/7L8yD51je4l0APWrl2LmzdvAgASEhJQVFSEHj16YNCgQRg6dCiysrIQGxsLBwcHvVYkQLrtaadOnbBjxw5MnDgR+fn5OHfuHLKysuQFzlAUCQB27doFGxsbxMTEYNiwYbh48SKuXr2KnJwceX70QZEA1swDACxcuBBcLhdxcXEYM2YMqqqqkJaWBktLS/j4+MDExERnu8IrCxHh8uXLGDduHMaNGwcA2Lp1KxISEmBvb49Dhw7h4MGDet08bYw5c+bIvx8+fBgnT56ElZUV7t27h++++05j27lqBJ2ZPvSAM2fOUFFRERERHT9+nPr27Us///wzVVVV0Y8//kihoaF08eJFHUvZMmKxmEaMGEG+vr4K5y9fvkwikYiIiFatWkW5ubm6EE9lxGIxbd68maKioujRo0dyy+nJkydJLBaTRCKhsLAwqqys1LGkiuhH/agjOnTogKCgIPTp0weurq7473//i127duHo0aMYP3483N3dYW9vr2sxm4WI8P7778PPzw8ODg5Yu3at/Fq/fv3A4/Fw4MABxMfHG4y3w8SJE/Ho0SPk5ubiu+++w9GjR1FdXY233noLXC4XsbGxSExMxNOnT3UtqiK61mZdIHtbExGFhISQnZ0d3b17l4iI7t+/T97e3nT48GFdiacSx48fpw0bNhAR0YMHD2jZsmXy2lYsFtOff/5JAwYMoFu3bulSTJVYunQpCYVCIiJav349jRw5khISEoiIKCkpiQYOHKiX+XnplEmmSHl5eRQVFUU5OTkUFxdHAwYMoL/++ovKysooLi6OHjx4oGNJlaOmpkb+PT8/n2bMmEGHDh2Sn3v69CkVFBToQjS1qKmpoU8//ZS2bt1KRERpaWk0evRo+uKLL4hI+sLQ1+bqS6dMRESFhYXUr18/OnHihPxcYmIieXl50bBhw6iwsFCH0qmHzGsjOTmZAgMD9fLNrSx5eXk0c+ZMWrp0KQUFBdH169cpNDRU718KL40175tvvsHMmTPRs2dPXLx4Ee+88w5cXV2xaNEiFBYWIiwsDPv27UOHDh3w6quv6lrcFmk4tsLhcEBEGDRoEN58801kZ2fD2dlZhxKqRv0xo65du2Lbtm3gcrkQiUS4evUqLly4gDZt2uhazGZ5aQwQ8+fPh42NDS5cuAAHBwc8fvwY27dvx7x58+Dp6YkrV64ojK7rIxKJBDNnzoRAIACXy33ObUZmYOjTpw88PT11IaJKNJYfmUK1b98e5ubmqKqqQmxsrP6ZwRtDxzWj1pH1kYqKimjjxo3k5uZGaWlp8uunT58mT09PuQFCn5k/fz7Z2NjQhAkT5P2G+k65V65c0ZVoatFSfm7cuEFERFVVVTqRT1WMVpkkEgmdP3+eMjIySCQS0ZQpU+jOnTv0559/UlBQEB0/fpyqq6tpwYIFBtO/uHPnDhERbdu2jUaPHk0CgUDh+qpVq6ioqEihQOozLeUnLCxMp17gqmKUvnkSiQRTp04Fl8tFWVkZCgsLYWlpKZ8Ml5KSgvnz5yMyMhKenp563xaXUVtbC1NTUwDAxo0bkZSUhF9//RW3bt1C586dDc67wdjyY5Q107vvvkvvvPMOEUmbCAMHDiQ7OztauXKl/E2XmppKOTk5OpRSeerXNPW/7927l5ycnGjw4MGUl5enC9HUwtjyI8MoDRCzZ8+GhYUFTp06BXNzc0yYMAELFiyAjY0N/v3vf6O0tBS+vr56729348YNAIrrNMisdgDQu3dviEQiREVFGcRb3Njy0xCjVKbg4GDMnj0be/fuxZYtW3D69Gn4+Phg6NCh+Mc//mEQy1xVVlYiJycHCxcuhK+vL27evCmfxCcrjLa2tjhz5gxcXFx0KapSGFt+GsNox5mCgoIgFosxZ84czJ49G35+fpBIJLCzszOIPpKFhQV69OiBpKQk2NrayguYbDyGiAzq7W1s+WkMozRA1Ofs2bPYtGkT5s+fj6FDh+panBaRTdwTiUQwMTHBiRMnkJeXB4FAgFWrViE/P9+gCp2x5ac5jF6ZACA5ORn29vbo0aOHrkVpFiICh8PBw4cPsWTJEjg4OGDKlCng8XhISEjAmTNnUFVVhf/85z/o2LGjrsVtEWPLT0u8FMpkSJSVlWHkyJFYsWIF4uPjcfPmTSxfvhyOjo64dOkSevfujddff13XYiqNseWnOZgy6RmPHz/GnTt30KtXL0ydOhWurq7Iy8vD4sWL4evrq2vxVMbY8tMcRmnNM2Q6d+4MHx8fxMbGYv78+Zg9ezaISO8nKTaFseWnOYzWmmeoyPoZZmZmiI+Px/nz53Hw4EG89tpruhZNLYwtP83Bmnk6Ij09HV5eXigqKsKxY8fg5eWFPn36wMRE+n4TiUQQCAQwNzc3iIJnbPlRB6ZMOiA+Ph4pKSnw9vbGnj17YGlpiZ49e+K1117DnDlz5AXQUDC2/KgL6zPpgL59+8LJyQlxcXGws7PD7t27ERwcjCdPnmDLli0G4aFRH2PLj7owZdIBNjY2GDVqFHx9fVFeXo6UlBT0798ffD4fYrEYlZWVuhZRJYwtP+rCmnk6pKSkBMeOHUNhYSG8vb0RGBiI6upqtG3bVteiqYWx5UdVWM2kZSQSCRYuXIibN28+N828U6dOGDVqFDp27IjLly/j2bNnel/wjC0/muTl6BnqkDlz5iAuLg7t2rXDrFmz4OTkpHDd2toa48aNg6mpKczNzXUkpfIYW340CWvmaZkHDx6gZ8+e2Lp1K+7evYtPPvkEzs7OBrO6akOMLT+ahCmTlqm/JNeGDRvw8OFDrFixAnfu3IGlpaXBzd0xtvxoEqZMLwCZFwAg3TDtxx9/BJfLRUxMjEFOPzC2/GgK1md6AcimZnM4HNja2iIvLw9Hjhwx2IJnbPnRFKxmesE8fPgQNTU16NWrl65F0QjGlp/WwJSJwdAQbJyJwdAQTJkYDA3BlInB0BBMmRgMDcGUicHQEEyZGAwNwZSJwdAQeu0BkZ+fj/z8fIjFYmRkZKC4uBjW1tbo168feDweunbt+tKPuitNfr70EIuBjAyguBiwtgb69QN4PKBrV+nBUB/tbbDResLCwghAk0dYWJjScSUmJtLixYuVujcjI4O8vb1p0aJFakr+P+7fv0/W1tbE5/OJz+fTsmXLiIjIy8uLiKS7/QUEBNDgwYPJ19eXHj582Oo0GyUsjAho+lDhWTbFhx9+SIMHD6arV69SfHy8mmKGkZubm/x5JSUlKfx33377Lfn4+NCgQYMoJCSk1TJrEr2umWxtbcHhcODn54eUlBQAQFRUFPbv34/k5GTY2tpqJd24uDiEhoZi7NixLd7bcKPmxuDz+Th48GCj18LDw7F9+3b06dMHz549095UBltbgMMB/PyAumeJqChg/34gOVl6vZVkZGTg4sWLSEpKwqlTpzB8+PAWwzT2/CIiIhAcHCz/nZSUBACoqKjAgQMHkJaWBg6HgydPnrRaZk2it30msViM1atXIzg4GHFxcfLz06ZNQ2JiIoKDg7FmzRqIxWKl47x69SpGjx4Nb29vXLt2DQBw8uRJBAQEwM/PDz///DOysrIQHR2NlStX4vvvv8e1a9cwaNAg+Pv7IyIiAgDw9ddf44MPPsCoUaNw9epVrF27Fnw+H4MHD5bHqyzm5uZISEjA06dPYW5urp2ZqWIxsHo1EBwM1HuWmDYNSEyUnl+zRnqfEhQWFiIoKAgBAQGYMGECxGIxvvjiC9y6dQuBgYHYtGkTDhw4gMDAQJSUlGDPnj3yZyzbvTEwMBBffPEF3nrrLaWzweVyUVxcjPT0dBCR/m0YreuqsSkSExMJAKWlpVFlZaW8aVdZWUlE0p3/AFBiYqLS8fn7+5NEIqGsrCwaPXo0SSQS8vPzI6FQSCKRiPz8/EgkElFYWBj99ttvREQUHBxMWVlZJJFIaNiwYXT//n0KCwuj5cuXExHRtWvXaMaMGURElJubS2PGjFFIt2Ez77vvviOi/zXzCgoK6OOPPyYHBweaNGmSPH8aJTFR2pRLSyOqrPxf006WVmqq9LeSz1IoFFJtbS0RES1YsIBOnTpFRP/LU/1mWXFxMb311lskkUiosrKS+Hw+ERHx+XxKSEh4Lu6Gzbxbt24pxBcfH08jRowge3t7io6OVvOBaAe9bebl5+cDANzc3Bq9Ljsvu08Z+vXrBw6HAxcXF+Tn5+PRo0e4ffu2vDlSWlqKR48eKYQpKCiQT3jz9PREdnY2AMDb2xsAkJWVhdTUVAQGBgIAeDzec+k218yzsbFBVFQUAGD58uX46aef8MknnyidJ6WQPaMmnqX8vJLP8vHjxwgJCcGTJ0+Ql5cHT0/PJu/Nzs7GjRs3EBQUBAAKz1f2DBvSsJlX/z8ePnw4hg8fjoqKCvj7+2Pq1KmwsLBQSm5to7fNPJmV7vr1641el51XxZqXmZkJIsJff/2Frl27wtraGr1798apU6eQlJSEzMzM51YbtbGxkS8ecvnyZTg4OACAvJ3fu3dv8Pl8JCUlISkpCSdPnlQpn3fu3JF/79Kly3OLlGgE2TNq4lnKzyv5LPfv34/g4GCcO3cOI0aMeE5mU1NTefO7V69e8PDwQGJiovwZy2ipr9mQ6upqCAQCANLN0/RtsRa9rZkCAgJgZ2eHtWvXYt++fQrXJBIJIiIiYG9vj4CAAKXjtLKywujRo1FYWIgffvgBXC4Xy5cvx7Bhw8DlctGlSxf85z//UQizZs0azJkzB0SEt99+G3Z2dgrXPTw84OjoCD6fDy6Xi2HDhuHLL79UuOfcuXPymsvV1RWRkZHya7/88guOHTsGc3NzdOzYETExMUrnR2kCAgA7O2DtWqDBs4REAkREAPb20vuUYMiQIZg+fTp+++23RhdNcXd3R2hoKCZOnIgdO3ZgypQp4PP54PF4cHd3x+bNm5uNPzQ0FOvXrwcALFq0CJaWlgCku7PPmjUL1dXVEIvFmD59ut7USgD0t89ERLRz507icDjk7+8v7zNFRUXR4MGDicPh0M6dO3UtouGwcycRh0Pk7/+/PlNUFNHgwdLz7Fm2Gr2tmQBAIBCAiORmcQAK/QlZlc9QAoFAqkL1niXq983Ys2w1ej3TlnlAaBDmAaF19FqZGAxDQm+teQyGocGUicHQEEyZGAwNwZSJwdAQTJkYDA3BlInB0BBMmRgMDcGUicHQEEyZGAwNwZSJwdAQTJkYDA3x/9OD5wXtNU3cAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 200x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x150 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import AutoMinorLocator\n",
    "\n",
    "def convert_potential(E_old, pH=13, Eref_old = 0.1635): \n",
    "    E_new = E_old + Eref_old + 0.059*pH\n",
    "    return E_new\n",
    "\n",
    "fig, axs = plt.subplots(ncols=1, nrows=1, constrained_layout = True, figsize = [2, 1.5])\n",
    "axs.tick_params(which = 'both', direction = 'in', labelsize = 6, bottom = True, left = True, top = True, right = True)\n",
    "axs.tick_params(axis = 'x', labelrotation = 45, labelsize = 6)\n",
    "axs.set_ylabel('Tafel slope \\n $\\mathrm{(mV \\ dec^{-1})}$', fontsize = 7)\n",
    "AllTafelData = AllTafelData.drop_duplicates()\n",
    "order_map = {\n",
    "    \"Pristine\": 0,\n",
    "    \"1 h at 1.73 V\": 1,\n",
    "    \"1 h at 1.93 V\": 2,\n",
    "}\n",
    "\n",
    "AllTafelData = (\n",
    "    AllTafelData.assign(_order=AllTafelData[\"info\"].map(order_map))\n",
    "      .sort_values(\"_order\")\n",
    "      .drop(columns=\"_order\")\n",
    ")\n",
    "\n",
    "\n",
    "#tafel slopes\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='Yes')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='Yes')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='Yes')]['error'], \n",
    "                linestyle = 'none', marker = 'o', capsize = 3, fillstyle = 'none', color = 'black', label = 'before EIS')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='No')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='No')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='No')]['error'], \n",
    "                linestyle = 'none', marker = 'o', capsize = 3, fillstyle = 'none', color = 'red', label = 'after EIS')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['error'], \n",
    "                linestyle = 'none', marker = 's', capsize = 3, fillstyle = 'none', color = 'black')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['error'], \n",
    "                linestyle = 'none', marker = 's', capsize = 3, fillstyle = 'none', color = 'red')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='Yes')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='Yes')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='Yes')]['error'], \n",
    "                linestyle = 'none', marker = '^', capsize = 3, fillstyle = 'none', color = 'black')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='No')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='No')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='No')]['error'], \n",
    "                linestyle = 'none', marker = '^', capsize = 3, fillstyle = 'none', color = 'red')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='Yes')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='Yes')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='Yes')]['error'], \n",
    "                linestyle = 'none', marker = '<', capsize = 3, fillstyle = 'none', color = 'black')\n",
    "\n",
    "axs.errorbar(AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='No')]['info'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='No')]['tafel slope'], \n",
    "                AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='No')]['error'], \n",
    "                linestyle = 'none', marker = '<', capsize = 3, fillstyle = 'none', color = 'red')\n",
    "axs.set_ylim(bottom = 0, top = 120)\n",
    "for ax in np.ravel(axs):\n",
    "    ax.margins(x=0.17, y=0.17)\n",
    "for ax in np.ravel(axs):\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_horizontalalignment('right')\n",
    "axs.yaxis.set_minor_locator(AutoMinorLocator(n = 2))\n",
    "fig.legend(bbox_to_anchor=(0.5, -0.05), loc = 'center', ncol = 2, fontsize = 6, frameon = False)\n",
    "plt.savefig(fname=f\"Tafelslopes_EQCM_SmallLoading.tif\", dpi=600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(ncols=1, nrows=1, constrained_layout = True, figsize = [2, 1.5])\n",
    "axs.tick_params(which = 'both', direction = 'in', labelsize = 6, bottom = True, left = True, top = True, right = True)\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='Yes')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='Yes')]['overpotential']),  linestyle = 'none', marker = 'o', fillstyle = 'none', color = 'black')\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='No')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#6') & (AllTafelData['before EIS']=='No')]['overpotential']), linestyle = 'none', marker = 'o', fillstyle = 'none', color = 'red')\n",
    "\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['overpotential']),  linestyle = 'none', marker = 's', fillstyle = 'none', color = 'black')\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['overpotential']), linestyle = 'none', marker = 's', fillstyle = 'none', color = 'red')\n",
    "\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='Yes')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['overpotential']),  linestyle = 'none', marker = '^', fillstyle = 'none', color = 'black')\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#11') & (AllTafelData['before EIS']=='No')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['overpotential']), linestyle = 'none', marker = '^', fillstyle = 'none', color = 'red')\n",
    "\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='Yes')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='Yes')]['overpotential']),  linestyle = 'none', marker = '<', fillstyle = 'none', color = 'black')\n",
    "axs.plot(AllTafelData[(AllTafelData['sample']=='#18') & (AllTafelData['before EIS']=='No')]['info'], convert_potential(AllTafelData[(AllTafelData['sample']=='#8') & (AllTafelData['before EIS']=='No')]['overpotential']), linestyle = 'none', marker = '<', fillstyle = 'none', color = 'red')\n",
    "\n",
    "\n",
    "\n",
    "axs.tick_params(axis = 'x', labelrotation = 45, labelsize = 6)\n",
    "axs.set_ylabel('Overpotential vs RHE \\n at 0.1 $\\mathrm{mA \\ cm^{-2} \\ (V)}$', fontsize = 7)\n",
    "\n",
    "for ax in np.ravel(axs):\n",
    "    ax.margins(x=0.17, y=0.17)\n",
    "for ax in np.ravel(axs):\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_horizontalalignment('right')\n",
    "\n",
    "axs.set_ylim(bottom = 1.01, top = 1.7)\n",
    "axs.yaxis.set_minor_locator(AutoMinorLocator(n = 2))\n",
    "fig.legend(bbox_to_anchor=(0.5, -0.05), loc = 'center', ncol = 2, fontsize = 6, frameon = False)\n",
    "plt.savefig(fname=f\"Overpotentials_EQCM_SmallLoading.tif\", dpi=600, pil_kwargs={\"compression\": \"tiff_lzw\"}, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72e8055b",
   "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>tafel slope</th>\n",
       "      <th>error</th>\n",
       "      <th>overpotential</th>\n",
       "      <th>info</th>\n",
       "      <th>before EIS</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.774025</td>\n",
       "      <td>0.703758</td>\n",
       "      <td>0.742386</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq5_09_CV_C01.mpr</th>\n",
       "      <td>75.892803</td>\n",
       "      <td>0.128195</td>\n",
       "      <td>0.714630</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>85.339898</td>\n",
       "      <td>0.208972</td>\n",
       "      <td>0.741697</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>69.355261</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>0.731392</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>77.535165</td>\n",
       "      <td>0.026083</td>\n",
       "      <td>0.719295</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq4_06_CV_C01.mpr</th>\n",
       "      <td>67.333172</td>\n",
       "      <td>0.054941</td>\n",
       "      <td>0.723507</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>80.497274</td>\n",
       "      <td>0.235142</td>\n",
       "      <td>0.732842</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_seq1_05_CV_C01.mpr</th>\n",
       "      <td>70.967225</td>\n",
       "      <td>0.064506</td>\n",
       "      <td>0.727310</td>\n",
       "      <td>Pristine</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>76.271633</td>\n",
       "      <td>0.093756</td>\n",
       "      <td>0.725029</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>81.298775</td>\n",
       "      <td>0.152826</td>\n",
       "      <td>0.721658</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>80.215607</td>\n",
       "      <td>0.227517</td>\n",
       "      <td>0.724418</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>75.266025</td>\n",
       "      <td>0.135410</td>\n",
       "      <td>0.724447</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>79.021256</td>\n",
       "      <td>0.032431</td>\n",
       "      <td>0.707082</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>83.734148</td>\n",
       "      <td>0.580130</td>\n",
       "      <td>0.739678</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_0p8V_MSE_eq_1p73V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>77.179163</td>\n",
       "      <td>0.125835</td>\n",
       "      <td>0.706690</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_0p8V_MSE_eq_1p73V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>94.454034</td>\n",
       "      <td>0.302221</td>\n",
       "      <td>0.739996</td>\n",
       "      <td>1 h at 1.73 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>86.451327</td>\n",
       "      <td>0.143573</td>\n",
       "      <td>0.710156</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>89.024878</td>\n",
       "      <td>0.594212</td>\n",
       "      <td>0.740460</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>91.056685</td>\n",
       "      <td>0.242149</td>\n",
       "      <td>0.736605</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_CA_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>72.850854</td>\n",
       "      <td>0.123063</td>\n",
       "      <td>0.718928</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>97.999830</td>\n",
       "      <td>0.096858</td>\n",
       "      <td>0.721691</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq4_06_CV_C01.mpr</th>\n",
       "      <td>92.690114</td>\n",
       "      <td>0.256467</td>\n",
       "      <td>0.733577</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq5_09_CV_C01.mpr</th>\n",
       "      <td>85.505755</td>\n",
       "      <td>0.084597</td>\n",
       "      <td>0.709119</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>No</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA_1h_at_1p0V_MSE_eq_1p93V_RHE_seq1_05_CV_C01.mpr</th>\n",
       "      <td>88.302871</td>\n",
       "      <td>0.035054</td>\n",
       "      <td>0.702502</td>\n",
       "      <td>1 h at 1.93 V</td>\n",
       "      <td>Yes</td>\n",
       "      <td>#18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    tafel slope     error  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.774025  0.703758   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    75.892803  0.128195   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    85.339898  0.208972   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...    69.355261  0.185000   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    77.535165  0.026083   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    67.333172  0.054941   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    80.497274  0.235142   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...    70.967225  0.064506   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...    76.271633  0.093756   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...    81.298775  0.152826   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...    80.215607  0.227517   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...    75.266025  0.135410   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    79.021256  0.032431   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    83.734148  0.580130   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    77.179163  0.125835   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    94.454034  0.302221   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...    86.451327  0.143573   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    89.024878  0.594212   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    91.056685  0.242149   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...    72.850854  0.123063   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    97.999830  0.096858   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...    92.690114  0.256467   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    85.505755  0.084597   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    88.302871  0.035054   \n",
       "\n",
       "                                                    overpotential  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.742386   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.714630   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.741697   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       0.731392   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       0.719295   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.723507   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       0.732842   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       0.727310   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.725029   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721658   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.724418   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...       0.724447   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.707082   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739678   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.706690   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.739996   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.710156   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.740460   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...       0.736605   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...       0.718928   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.721691   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...       0.733577   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.709119   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...       0.702502   \n",
       "\n",
       "                                                             info before EIS  \\\n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine         No   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine         No   \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...       Pristine        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...       Pristine        Yes   \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.73 V        Yes   \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V        Yes   \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.73 V         No   \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...  1 h at 1.73 V         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V        Yes   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V         No   \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.73 V         No   \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.73 V        Yes   \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V         No   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V         No   \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...  1 h at 1.93 V        Yes   \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...  1 h at 1.93 V        Yes   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V        Yes   \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...  1 h at 1.93 V         No   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V         No   \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...  1 h at 1.93 V        Yes   \n",
       "\n",
       "                                                   sample  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250526_001_#8_Co3O4Nps_CV_5mVpers_initial_s...     #8  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_initial_...    #18  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250529_001_#11_Co3O4Nps_CV_5mVpers_initial_...    #11  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_initial_s...     #6  \n",
       "CL20250522_006_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250526_003_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250526_004_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250522_006_#6_Co3O4Nps_CVafter1h_CA_at_0p8V...     #6  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250616_003_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250530_002_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250523_003_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250530_003_#11_Co3O4Nps_CV_5mVpers_after1h_...    #11  \n",
       "CL20250523_002_#6_Co3O4Nps_CV_5mVpers_after1h_C...     #6  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250527_001_#8_Co3O4Nps_CV_5mVpers_after_CA_...     #8  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  \n",
       "CL20250617_001_#18_Co3O4Nps_CV_5mVpers_after_CA...    #18  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllTafelData.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff1810ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
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