Nest markdown section headings below the main heading (#511)

notebooks/quickstart.ipynb

@@ -2086,7 +2086,7 @@
    "id": "40c02f81-7e0d-44f3-8311-88867083f6f3",
    "metadata": {},
    "source": [
-    "# Storage\n",
+    "## Storage\n",
     "\n",
     "You've run you workflow and want to close your notebook for the day -- but you don't want to lose your data state. No problem! Just `.save()` your workflow. We'll go back and use our e-mail workflow as an example:"
    ]
@@ -2312,7 +2312,7 @@
    "id": "53d72537-6db7-4b42-b729-fa8a16cc5815",
    "metadata": {},
    "source": [
-    "# Failure recovery\n",
+    "## Failure recovery\n",
     "\n",
     "If a graph raises an exception, the default behaviour is to save (pickle) a copy of the node into its directoy under the filename \"recovery\". These can then be manually re-loaded to investigate the failure state:"
    ]
@@ -2784,7 +2784,7 @@
    "id": "4c7f5d84-d9f6-4eb5-9cf0-56494c53110c",
    "metadata": {},
    "source": [
-    "# Parallelization\n",
+    "## Parallelization\n",
     "\n",
     "`pyiron_workflow` actually splits apart data channels and the flow of data from \"signal\" channels and the flow of execution. This is important for while-loop flows, and if you want to learn more go check out the `deepdive.ipynb`. Most of the time, workflows form a Directed Acyclic Graph (DAG), and this execution flow can be completely automated -- you only need to define the flow of data.\n",
     "\n",
@@ -2855,7 +2855,7 @@
    "id": "5a66e5fe-7fdc-4c31-93ff-b1e21e9dd86c",
    "metadata": {},
    "source": [
-    "# For-loops\n",
+    "## For-loops\n",
     "\n",
     "You can quickly iterate over node instances to get a `pandas.DataFrame` linking looped input to output (non-looped input is accessible elsewhere, like in the node's input channels). This comes in two flavours: nested loops with `iter`:"
    ]