diff --git a/examples/getting_started.ipynb b/examples/getting_started.ipynb
index 870c6e19e3..7621e5f6a6 100644
--- a/examples/getting_started.ipynb
+++ b/examples/getting_started.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "id": "1cc50017",
    "metadata": {},
    "outputs": [
@@ -32,7 +32,7 @@
      "text": [
       "\n",
       "══════════════════════════════════════════════════════════════════════════════\n",
-      "net : <openpnm.network.Cubic at 0x7fafd7ccd270>\n",
+      "net : <openpnm.network.Cubic at 0x7f7997db1360>\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       "  #  Properties                                                   Valid Values\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "id": "39c7e190-2caf-4fa8-ba76-d56fae4d07b8",
    "metadata": {},
    "outputs": [
@@ -108,7 +108,7 @@
      "text": [
       "\n",
       "══════════════════════════════════════════════════════════════════════════════\n",
-      "phase_01 : <openpnm.phase.Mercury at 0x7fafd81efae0>\n",
+      "phase_01 : <openpnm.phase.Mercury at 0x7f798708fea0>\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       "  #  Properties                                                   Valid Values\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
@@ -148,14 +148,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "cb0f3ee8-2d91-4a28-9bda-6e1abf363da9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d9714883576413eab79e65b26230b13",
+       "model_id": "a08646409dcb47d3a2d26aef010a5c20",
        "version_major": 2,
        "version_minor": 0
       },
@@ -175,21 +175,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "id": "3c60b5ed-f60e-4e98-9188-d2431cbe7c52",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 550x400 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 463,
-       "width": 623
+       "height": 383,
+       "width": 534
       }
      },
      "output_type": "display_data"
@@ -197,10 +197,10 @@
    ],
    "source": [
     "data = mip.pc_curve()\n",
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
     "ax.semilogx(data.pc, data.snwp, 'k-o')\n",
-    "ax.set_xlabel('Capillary Pressure [Pa]')\n",
-    "ax.set_ylabel('Mercury Saturation');"
+    "ax.set_xlabel('capillary pressure [Pa]')\n",
+    "ax.set_ylabel('mercury saturation');"
    ]
   },
   {
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "id": "aca961f1",
    "metadata": {},
    "outputs": [],
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 6,
    "id": "a9e72cc5",
    "metadata": {},
    "outputs": [
@@ -253,7 +253,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[6.43550367e-13]\n"
+      "[6.44364084e-13]\n"
      ]
     }
    ],
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "id": "ab23c414",
    "metadata": {},
    "outputs": [],
@@ -301,21 +301,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "id": "1aae9dfd",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 600x450 with 1 Axes>"
       ]
      },
      "metadata": {
       "image/png": {
-       "height": 464,
-       "width": 623
+       "height": 434,
+       "width": 584
       }
      },
      "output_type": "display_data"
@@ -324,9 +324,11 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(6, 4.5))\n",
     "ax.hist(pn['pore.diameter'], edgecolor='k', label='new diameter')\n",
-    "ax.hist(pn['pore.old_diameter'], edgecolor='k', label='old_diameter')\n",
+    "ax.hist(pn['pore.old_diameter'], edgecolor='k', label='old_diameter', bins=20)\n",
+    "ax.set_xlabel('diameter [m]')\n",
+    "ax.set_ylabel('frequency')\n",
     "ax.legend();"
    ]
   },
diff --git a/openpnm/__version__.py b/openpnm/__version__.py
index 5f96809cf1..c0ac513612 100644
--- a/openpnm/__version__.py
+++ b/openpnm/__version__.py
@@ -1 +1 @@
-__version__ = '3.4.1.dev0'
+__version__ = '3.4.1.dev5'
diff --git a/openpnm/_skgraph/generators/tools/_funcs.py b/openpnm/_skgraph/generators/tools/_funcs.py
index 87f51ac151..1791f682c3 100644
--- a/openpnm/_skgraph/generators/tools/_funcs.py
+++ b/openpnm/_skgraph/generators/tools/_funcs.py
@@ -85,7 +85,7 @@ def get_centroid(pts, mode='rigorous'):
     if mode == 'rigorous':
         tri = sptl.Delaunay(pts)
         CoM = center_of_mass(simplices=tri.simplices.astype(np.int_),
-                             points=tri.points.astype(np.float_))
+                             points=tri.points.astype(float))
     elif mode == 'fast':
         CoM = pts.mean(axis=0)
     return CoM
@@ -99,11 +99,11 @@ def get_centroid(pts, mode='rigorous'):
 # @njit
 def center_of_mass(simplices, points):
     A = []
-    centroids = np.zeros((simplices.shape[0], points.shape[1]), dtype=np.float_)
+    centroids = np.zeros((simplices.shape[0], points.shape[1]), dtype=float)
     for i, s in enumerate(simplices):
         xy = points[s]
         cen = np.sum(points[s], axis=0)/simplices.shape[1]
-        centroids[i, :] = cen.astype(np.float_)
+        centroids[i, :] = cen.astype(float)
         if xy.shape[1] == 2:  # In 2D
             # Use Heron's formula to find area of arbitrary triangle
             a = np.sqrt((xy[0, 0] - xy[1, 0])**2 + (xy[0, 1] - xy[1, 1])**2)
@@ -117,9 +117,9 @@ def center_of_mass(simplices, points):
             temp = np.abs(np.linalg.det(d))/6.0
             # temp = 0
         A.append(temp)
-    A = np.array(A, dtype=np.float_)
+    A = np.array(A, dtype=float)
     CoM = np.array([(centroids[:, i]*A/A.sum()*len(A)).mean()
-                    for i in range(centroids.shape[1])], dtype=np.float_)
+                    for i in range(centroids.shape[1])], dtype=float)
     return CoM
 
 
diff --git a/openpnm/algorithms/_reactive_transport.py b/openpnm/algorithms/_reactive_transport.py
index abbae740eb..04fe587564 100644
--- a/openpnm/algorithms/_reactive_transport.py
+++ b/openpnm/algorithms/_reactive_transport.py
@@ -1,14 +1,16 @@
 import logging
 import sys
-
 import numpy as np
 from numpy.linalg import norm
-from scipy.optimize.nonlin import TerminationCondition
+try:  # For scipy < 1.14
+    from scipy.optimize.nonlin import TerminationCondition
+except ImportError:  # For newer Scipy
+    from scipy.optimize._nonlin import TerminationCondition
 from tqdm.auto import tqdm
-
 from openpnm.algorithms import Transport
 from openpnm.utils import Docorator, TypedList
 
+
 __all__ = ["ReactiveTransport"]
 
 
diff --git a/openpnm/models/geometry/pore_surface_area/_funcs.py b/openpnm/models/geometry/pore_surface_area/_funcs.py
index 655d870a38..f5b6bf1b43 100644
--- a/openpnm/models/geometry/pore_surface_area/_funcs.py
+++ b/openpnm/models/geometry/pore_surface_area/_funcs.py
@@ -1,5 +1,9 @@
 import numpy as _np
 from openpnm.models.geometry import _geodocs
+import logging
+
+
+logger = logging.getLogger(__name__)
 
 
 __all__ = ["sphere",
@@ -13,7 +17,8 @@
 def sphere(
     network,
     pore_diameter='pore.diameter',
-    throat_cross_sectional_area='throat.cross_sectional_area'
+    throat_cross_sectional_area='throat.cross_sectional_area',
+    amin=0,
 ):
     r"""
     Calculates internal surface area of pore bodies assuming they are spheres,
@@ -24,6 +29,8 @@ def sphere(
     %(network)s
     %(Dp)s
     %(Act)s
+    amin : float
+        The lower limit for surface area, useful for preventing
 
     Returns
     -------
@@ -42,7 +49,10 @@ def sphere(
     Tca = network[throat_cross_sectional_area]
     _np.subtract.at(value, network.conns[:, 0], Tca)
     _np.subtract.at(value, network.conns[:, 1], Tca)
-    value = _np.clip(value, 1e-12, _np.inf)
+    if amin is not None:
+        value = _np.clip(value, amin, _np.inf)
+    elif _np.any(value < 0):
+        logger.warn('Negative pore surface areas found')
     return value
 
 
@@ -50,7 +60,8 @@ def sphere(
 def circle(
     network,
     pore_diameter='pore.diameter',
-    throat_cross_sectional_area='throat.cross_sectional_area'
+    throat_cross_sectional_area='throat.cross_sectional_area',
+    amin=0,
 ):
     r"""
     Calculates internal surface area of pore bodies assuming they are
@@ -61,6 +72,8 @@ def circle(
     %(network)s
     %(Dp)s
     %(Act)s
+    amin : float
+        The lower limit for surface area, useful for preventing
 
     Returns
     -------
@@ -73,14 +86,18 @@ def circle(
     Tca = network[throat_cross_sectional_area]
     _np.subtract.at(value, network.conns[:, 0], Tca)
     _np.subtract.at(value, network.conns[:, 1], Tca)
-    value = _np.clip(value, 1e-12, _np.inf)
+    if amin is not None:
+        value = _np.clip(value, amin, _np.inf)
+    elif _np.any(value < 0):
+        logger.warn('Negative pore surface areas found')
     return value
 
 
 def cube(
     network,
     pore_diameter='pore.diameter',
-    throat_cross_sectional_area='throat.cross_sectional_area'
+    throat_cross_sectional_area='throat.cross_sectional_area',
+    amin=0,
 ):
     r"""
     Calculates internal surface area of pore bodies assuming they are cubes
@@ -91,6 +108,8 @@ def cube(
     %(network)s
     %(Dp)s
     %(Act)s
+    amin : float
+        The lower limit for surface area, useful for preventing
 
     Returns
     -------
@@ -101,14 +120,18 @@ def cube(
     Tca = network[throat_cross_sectional_area]
     _np.subtract.at(value, network.conns[:, 0], Tca)
     _np.subtract.at(value, network.conns[:, 1], Tca)
-    value = _np.clip(value, 1e-12, _np.inf)
+    if amin is not None:
+        value = _np.clip(value, amin, _np.inf)
+    elif _np.any(value < 0):
+        logger.warn('Negative pore surface areas found')
     return value
 
 
 def square(
     network,
     pore_diameter='pore.diameter',
-    throat_cross_sectional_area='throat.cross_sectional_area'
+    throat_cross_sectional_area='throat.cross_sectional_area',
+    amin=0,
 ):
     r"""
     Calculates internal surface area of pore bodies assuming they are
@@ -119,6 +142,8 @@ def square(
     %(network)s
     %(Dp)s
     %(Act)s
+    amin : float
+        The lower limit for surface area, useful for preventing
 
     Returns
     -------
@@ -129,5 +154,8 @@ def square(
     Tca = network[throat_cross_sectional_area]
     _np.subtract.at(value, network.conns[:, 0], Tca)
     _np.subtract.at(value, network.conns[:, 1], Tca)
-    value = _np.clip(value, 1e-12, _np.inf)
+    if amin is not None:
+        value = _np.clip(value, amin, _np.inf)
+    elif _np.any(value < 0):
+        logger.warn('Negative pore surface areas found')
     return value
diff --git a/openpnm/solvers/_scipy.py b/openpnm/solvers/_scipy.py
index 2ade2ce25f..d4700481aa 100644
--- a/openpnm/solvers/_scipy.py
+++ b/openpnm/solvers/_scipy.py
@@ -23,4 +23,7 @@ def solve(self, A, b, **kwargs):
         if not isinstance(A, (csr_matrix, csc_matrix)):
             A = A.tocsr()
         atol = self._get_atol(b)
-        return cg(A, b, tol=self.tol, atol=atol, **kwargs)
+        try:
+            return cg(A, b, tol=self.tol, atol=atol, **kwargs)
+        except TypeError:
+            return cg(A, b, rtol=self.tol, atol=atol, **kwargs)
diff --git a/openpnm/utils/__init__.py b/openpnm/utils/__init__.py
index 1b52e093bd..b1036b9405 100644
--- a/openpnm/utils/__init__.py
+++ b/openpnm/utils/__init__.py
@@ -17,38 +17,13 @@
 
 def _get_version():
     from openpnm.__version__ import __version__ as ver
+
     suffix = ".dev0"
     if ver.endswith(suffix):
-        ver = ver[:-len(suffix)]
+        ver = ver[: -len(suffix)]
     return ver
 
 
-def _setup_logger():
-    import os
-    import logging
-    # You can add info to the logger message by inserting the desired %(item)
-    # For a list of available items see:
-    # https://docs.python.org/3/library/logging.html#logrecord-attributes
-    # NOTE: If the calling locations appears as 'root' it's because the logger
-    # was not given a name in a file somewhere.  A good option is __name__.
-
-    try:
-        terminal_width = os.get_terminal_size().columns
-    except OSError:
-        terminal_width = 60
-    column_width = terminal_width if terminal_width < 60 else 60
-
-    log_format = \
-        '-' * column_width + '\n\
-        - %(levelname)-7s: %(message)s \n\
-        - SOURCE : %(name)s.%(funcName)s \n\
-        - TIME   : %(asctime)s\
-        \n' + '-' * column_width
-
-    logging.basicConfig(level=logging.WARNING, format=log_format)
-    del log_format
-
-
 def _setup_logger_rich():
     import logging
     from rich.logging import RichHandler
@@ -57,3 +32,6 @@ def _setup_logger_rich():
     logging.basicConfig(
         format=FORMAT, datefmt="[%X]", handlers=[RichHandler(rich_tracebacks=True)]
     )
+
+
+_setup_logger_rich()
diff --git a/openpnm/visualization/_plottools.py b/openpnm/visualization/_plottools.py
index 1f01665177..1ef47c59fd 100644
--- a/openpnm/visualization/_plottools.py
+++ b/openpnm/visualization/_plottools.py
@@ -602,7 +602,7 @@ def plot_tutorial(network,
     fig = plt.gcf()
     fig.tight_layout()
     dims = op.topotools.dimensionality(network)
-    xy_range = network.coords.ptp(axis=0)[dims]
+    xy_range = np.ptp(network.coords, axis=0)[dims]
     aspect_ratio = xy_range[0] / xy_range[1]
     fig.set_size_inches(5, 5 / aspect_ratio)
 
@@ -781,7 +781,7 @@ def _generate_voxel_image(network, pore_shape, throat_shape, max_dim=200):
     # Transform points to satisfy origin at (0, 0, 0)
     xyz0 = xyz.min(axis=0) - delta
     xyz += -xyz0
-    res = (xyz.ptp(axis=0).max() + 2 * delta) / max_dim
+    res = (np.ptp(xyz, axis=0).max() + 2 * delta) / max_dim
     shape = np.rint((xyz.max(axis=0) + delta) / res).astype(int) + 2 * extra_clearance
 
     # Transforming from real coords to matrix coords
@@ -983,7 +983,7 @@ def set_mpl_style():  # pragma: no cover
     image_props = {'interpolation': 'none',
                    'cmap': 'viridis'}
     line_props = {'linewidth': 2,
-                  'markersize': 8,
+                  'markersize': 7,
                   'markerfacecolor': 'w'}
     font_props = {'size': sfont}
     axes_props = {'titlesize': lfont,
diff --git a/setup.cfg b/setup.cfg
index e3cdbf7308..9bcaa79699 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 3.4.1.dev0
+current_version = 3.4.1.dev5
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)\.(?P<release>\D+)(?P<build>\d+)?
 serialize = {major}.{minor}.{patch}.{release}{build}
 
diff --git a/tests/unit/models/geometry/PoreSurfaceAreaTest.py b/tests/unit/models/geometry/PoreSurfaceAreaTest.py
index af7e0d938e..515c57694e 100644
--- a/tests/unit/models/geometry/PoreSurfaceAreaTest.py
+++ b/tests/unit/models/geometry/PoreSurfaceAreaTest.py
@@ -2,6 +2,7 @@
 import openpnm.models.geometry.pore_surface_area as mods
 import numpy as np
 from numpy.testing import assert_allclose
+import pytest
 
 
 class PoreSurfaceAreaTest:
@@ -69,6 +70,22 @@ def test_circle_multi_geom(self):
         assert_allclose(a, b)
         assert_allclose(c, d)
 
+    def test_negative_surface_area(self):
+        pn = op.network.Cubic([2, 1, 1])
+        pn.add_model_collection(op.models.collections.geometry.circles_and_rectangles)
+        pn.regenerate_models()
+        pn.add_model(propname='pore.surface_area', model=op.models.geometry.pore_surface_area.circle)
+        pn['throat.cross_sectional_area'] = 100
+        pn.regenerate_models('pore.surface_area@all')
+        assert np.all(pn['pore.surface_area'] == 0)
+        with pytest.warns():
+            pn.add_model(
+                propname='pore.surface_area',
+                model=op.models.geometry.pore_surface_area.circle,
+                amin=None,
+            )
+        assert np.all(pn['pore.surface_area'] < 0)
+
 
 if __name__ == '__main__':