diff --git a/TOC_Analysis/20260909/IC_Calibration.cal b/TOC_Analysis/20260909/IC_Calibration.cal
new file mode 100644
index 0000000..84666eb
--- /dev/null
+++ b/TOC_Analysis/20260909/IC_Calibration.cal
@@ -0,0 +1,25 @@
+METADATA
+----------------------------------------------------------------------
+Standard			2	mg / L TOC
+Standard Prep. Date		10.09.2026
+Measurement Date		10.09.2026
+
+CALIBRATION DATA
+conc. STD	Dilution	A1		A2		A3
+(mg / L)	(-)		(-)		(-)		(-)
+0.05		40.0		0.3451		0.3456		0.3914
+0.10		20.0		0.6457		0.6743		0.5987
+0.20		10.0		0.9987		1.0544		1.0343
+0.40		5.00		1.3654		1.4102		1.3862
+1.00		2.00		2.3302		2.2963		2.3417
+2.00		1.00		4.3698		4.1897		4.4005
+
+CALIBRATION RESULTS
+----------------------------------------------------------------------
+Slope		10.3656
+Intercept	0.33560
+R2		0.99740
+
+FILE		IC_Calibration.cal
+COMPUTED	10.09.2026 17:45:36
+----------------------------------------------------------------------
diff --git a/TOC_Analysis/20260909/NPOC_Calibration.cal b/TOC_Analysis/20260909/NPOC_Calibration.cal
new file mode 100644
index 0000000..5067a2e
--- /dev/null
+++ b/TOC_Analysis/20260909/NPOC_Calibration.cal
@@ -0,0 +1,25 @@
+METADATA
+----------------------------------------------------------------------
+Standard			2	mg / L TOC
+Standard Prep. Date		10.09.2026
+Measurement Date		10.09.2026
+
+CALIBRATION DATA
+conc. STD	Dilution	A1		A2		A3
+(mg / L)	(-)		(-)		(-)		(-)
+0.05		40.0		0.3451		0.3456		0.3914
+0.10		20.0		0.6457		0.6743		0.5987
+0.20		10.0		0.9987		1.0544		1.0343
+0.40		5.00		1.3654		1.4102		1.3862
+1.00		2.00		2.3302		2.2963		2.3417
+2.00		1.00		4.3698		4.1897		4.4005
+
+CALIBRATION RESULTS
+----------------------------------------------------------------------
+Slope		10.3656
+Intercept	0.33560
+R2		0.99740
+
+FILE		NPOC_Calibration.cal
+COMPUTED	10.09.2026 17:45:36
+----------------------------------------------------------------------
\ No newline at end of file
diff --git a/TOC_Analysis/20260909/TC_Calibration.cal b/TOC_Analysis/20260909/TC_Calibration.cal
new file mode 100644
index 0000000..59427bd
--- /dev/null
+++ b/TOC_Analysis/20260909/TC_Calibration.cal
@@ -0,0 +1,25 @@
+METADATA
+----------------------------------------------------------------------
+Standard			2	mg / L TOC
+Standard Prep. Date		10.09.2026
+Measurement Date		10.09.2026
+
+CALIBRATION DATA
+conc. STD	Dilution	A1		A2		A3
+(mg / L)	(-)		(-)		(-)		(-)
+0.05		40.0		0.3451		0.3456		0.3914
+0.10		20.0		0.6457		0.6743		0.5987
+0.20		10.0		0.9987		1.0544		1.0343
+0.40		5.00		1.3654		1.4102		1.3862
+1.00		2.00		2.3302		2.2963		2.3417
+2.00		1.00		4.3698		4.1897		4.4005
+
+CALIBRATION RESULTS
+----------------------------------------------------------------------
+Slope		10.3656
+Intercept	0.33560
+R2		0.99740
+
+FILE		TC_Calibration.cal
+COMPUTED	10.09.2026 17:45:36
+----------------------------------------------------------------------
\ No newline at end of file
diff --git a/TOC_Analysis/20260909/analysis.ipynb b/TOC_Analysis/20260909/analysis.ipynb
new file mode 100644
index 0000000..1cd31ed
--- /dev/null
+++ b/TOC_Analysis/20260909/analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "393a8a7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from defs.TOC import TC_Calibration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f2cf26e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dilutions = [10, 7, 5, 3, 2]\n",
+    "NPOCs = [7.136, 4.120, 3.552, 3.430, 3.255]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d81f0103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fitfunc(x, a):\n",
+    "    return np.divide(a, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d8d8a1af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111)\n",
+    "\n",
+    "parameter = curve_fit(fitfunc, xdata = [1 / dilution for dilution in dilutions], ydata=NPOCs, p0 = [7])\n",
+    "xs = np.divide(1, np.linspace(start = 1, stop = 11, num = 1000))\n",
+    "ys = fitfunc(xs, parameter[0][0])\n",
+    "\n",
+    "ax.scatter([1 / dilution for dilution in dilutions], NPOCs)\n",
+    "ax.plot(xs, ys, linestyle = '--')\n",
+    "\n",
+    "\n",
+    "ax.set_xlabel('1 / Dilution')\n",
+    "ax.set_ylabel('NPOC (mg/L)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00b59d82",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "MA",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}