diff --git a/docs/notebooks/ex1-titration.ipynb b/docs/notebooks/ex1-titration.ipynb
index cb0b60f..c63dd88 100644
--- a/docs/notebooks/ex1-titration.ipynb
+++ b/docs/notebooks/ex1-titration.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,10 +162,8 @@
     "\n",
     "\n",
     "# Initialize concentration vectors\n",
-    "cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "cs = np.zeros(n_cells * n_species, dtype=np.float64)\n",
-    "phr.rm_get_concentrations(cc)\n",
-    "phr.rm_get_species_concentrations(cs)\n",
+    "cc = phr.rm.GetConcentrations()\n",
+    "cs = phr.rm.GetSpeciesConcentrations()\n",
     "\n",
     "# HCl concentration vector\n",
     "hcl = np.linspace(hcl_range[0], hcl_range[1], n_cells)  # mol/L\n",
@@ -175,7 +173,7 @@
     "indx_h = np.where(species == \"H+\")[0][0]\n",
     "\n",
     "# Add HCl\n",
-    "cs_r = cs.reshape(n_species, n_cells).T\n",
+    "cs_r = np.array(cs).reshape(n_species, n_cells).T\n",
     "cs_r[:, indx_cl] += hcl  # Cl-\n",
     "cs_r[:, indx_h] += hcl  # H+\n",
     "cs1 = cs_r.T.reshape(n_cells * n_species)  # Updated concentrations (after adding HCl)"
@@ -205,13 +203,13 @@
    ],
    "source": [
     "# Tranfer the modified species concentrations back to PhreeqcRM\n",
-    "phr.rm_species_concentrations2_module(cs1)\n",
-    "phr.rm_set_time(1.0)\n",
-    "phr.rm_set_time_step(1.0)\n",
+    "phr.rm.SpeciesConcentrations2Module(cs1)\n",
+    "phr.rm.SetTime(1.0)\n",
+    "phr.rm.SetTimeStep(1.0)\n",
     "\n",
     "# Run the simulation\n",
     "start_time = time.time()\n",
-    "phr.rm_run_cells()\n",
+    "phr.rm.RunCells()\n",
     "elapsed = time.time() - start_time\n",
     "print(f\"Simulation completed in {elapsed:.2f} seconds\")"
    ]
diff --git a/docs/notebooks/ex2-BTEX_dissolution.ipynb b/docs/notebooks/ex2-BTEX_dissolution.ipynb
index fdcf44a..90ff44d 100644
--- a/docs/notebooks/ex2-BTEX_dissolution.ipynb
+++ b/docs/notebooks/ex2-BTEX_dissolution.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,10 +147,8 @@
     "n_species = len(phr.species)\n",
     "\n",
     "# Get initial concentrations\n",
-    "cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "cs = np.zeros(n_cells * n_species, dtype=np.float64)\n",
-    "phr.rm_get_concentrations(cc)\n",
-    "phr.rm_get_species_concentrations(cs)\n",
+    "cc = np.array(phr.rm.GetConcentrations())\n",
+    "cs = np.array(phr.rm.GetSpeciesConcentrations())\n",
     "\n",
     "# Time step setup\n",
     "dt = sim_duration / n_steps * 24 * 3600.0  # Convert days to seconds\n",
@@ -179,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -197,12 +195,12 @@
     "    time_vector[step] = current_time\n",
     "\n",
     "    # Run simulation step\n",
-    "    phr.rm_set_time(current_time)\n",
-    "    phr.rm_set_time_step(dt)\n",
-    "    status = phr.rm_run_cells()\n",
+    "    phr.rm.SetTime(current_time)\n",
+    "    phr.rm.SetTimeStep(dt)\n",
+    "    phr.rm.RunCells()\n",
     "\n",
     "    # Store results\n",
-    "    status = phr.rm_get_concentrations(cc)\n",
+    "    cc = np.array(phr.rm.GetConcentrations())\n",
     "    concentration_results[step, :] = cc[component_map]\n",
     "\n",
     "elapsed = time.time() - start_time\n",
@@ -218,12 +216,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 2 Axes>"
       ]
diff --git a/docs/notebooks/ex3-BTEX_dissolution_transport_coupling.ipynb b/docs/notebooks/ex3-BTEX_dissolution_transport_coupling.ipynb
index 72584af..39f8b47 100644
--- a/docs/notebooks/ex3-BTEX_dissolution_transport_coupling.ipynb
+++ b/docs/notebooks/ex3-BTEX_dissolution_transport_coupling.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,8 +122,7 @@
     "    n_comps = len(components)\n",
     "\n",
     "    # Get initial concentrations\n",
-    "    cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "    phr.rm_get_concentrations(cc)\n",
+    "    cc = np.array(phr.rm.GetConcentrations())\n",
     "\n",
     "    # Prepare monitoring\n",
     "    monitored_species = [\"Benz\", \"Ethyl\"]\n",
@@ -153,10 +152,10 @@
     "        time_vector[step] = current_time\n",
     "\n",
     "        # 1) Reactive step\n",
-    "        phr.rm_set_time(current_time * 3600 * 24)  # Convert to seconds\n",
-    "        phr.rm_set_time_step(dt * 3600 * 24)\n",
-    "        phr.rm_run_cells()\n",
-    "        phr.rm_get_concentrations(cc)\n",
+    "        phr.rm.SetTime(current_time * 3600 * 24)  # Convert to seconds\n",
+    "        phr.rm.SetTimeStep(dt * 3600 * 24)\n",
+    "        phr.rm.RunCells()\n",
+    "        cc = np.array(phr.rm.GetConcentrations())\n",
     "        conc_matrix = cc.reshape((n_comps, n_cells)).T\n",
     "\n",
     "        # 2) Transport step\n",
@@ -176,7 +175,7 @@
     "\n",
     "        # Update concentrations in PhreeqcRM\n",
     "        cc = conc_matrix.T.flatten()\n",
-    "        phr.rm_set_concentrations(cc)\n",
+    "        phr.rm.SetConcentrations(cc)\n",
     "\n",
     "    elapsed = time.time() - start_time\n",
     "    print(f\"Simulation completed in {elapsed:.2f} seconds\")\n",
@@ -285,15 +284,15 @@
      "output_type": "stream",
      "text": [
       "Running equilibrium dissolution simulation...\n",
-      "Simulation completed in 13.72 seconds\n",
+      "Simulation completed in 15.50 seconds\n",
       "\n",
       "Running kinetic dissolution simulation...\n",
-      "Simulation completed in 13.89 seconds\n"
+      "Simulation completed in 15.55 seconds\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
diff --git a/examples/ex1-titration.ipynb b/examples/ex1-titration.ipynb
index cb0b60f..c63dd88 100644
--- a/examples/ex1-titration.ipynb
+++ b/examples/ex1-titration.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,10 +162,8 @@
     "\n",
     "\n",
     "# Initialize concentration vectors\n",
-    "cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "cs = np.zeros(n_cells * n_species, dtype=np.float64)\n",
-    "phr.rm_get_concentrations(cc)\n",
-    "phr.rm_get_species_concentrations(cs)\n",
+    "cc = phr.rm.GetConcentrations()\n",
+    "cs = phr.rm.GetSpeciesConcentrations()\n",
     "\n",
     "# HCl concentration vector\n",
     "hcl = np.linspace(hcl_range[0], hcl_range[1], n_cells)  # mol/L\n",
@@ -175,7 +173,7 @@
     "indx_h = np.where(species == \"H+\")[0][0]\n",
     "\n",
     "# Add HCl\n",
-    "cs_r = cs.reshape(n_species, n_cells).T\n",
+    "cs_r = np.array(cs).reshape(n_species, n_cells).T\n",
     "cs_r[:, indx_cl] += hcl  # Cl-\n",
     "cs_r[:, indx_h] += hcl  # H+\n",
     "cs1 = cs_r.T.reshape(n_cells * n_species)  # Updated concentrations (after adding HCl)"
@@ -205,13 +203,13 @@
    ],
    "source": [
     "# Tranfer the modified species concentrations back to PhreeqcRM\n",
-    "phr.rm_species_concentrations2_module(cs1)\n",
-    "phr.rm_set_time(1.0)\n",
-    "phr.rm_set_time_step(1.0)\n",
+    "phr.rm.SpeciesConcentrations2Module(cs1)\n",
+    "phr.rm.SetTime(1.0)\n",
+    "phr.rm.SetTimeStep(1.0)\n",
     "\n",
     "# Run the simulation\n",
     "start_time = time.time()\n",
-    "phr.rm_run_cells()\n",
+    "phr.rm.RunCells()\n",
     "elapsed = time.time() - start_time\n",
     "print(f\"Simulation completed in {elapsed:.2f} seconds\")"
    ]
diff --git a/examples/ex1-titration.py b/examples/ex1-titration.py
index 015289f..33cca81 100644
--- a/examples/ex1-titration.py
+++ b/examples/ex1-titration.py
@@ -2,10 +2,10 @@
 MiBiReMo - Example - Calculate calcite titration curve.
 
 This script models the reaction:
-CaCO3(s) + 2HCl(aq) → CaCl2(aq) + CO2(g) + H2O(l)
+CaCO3(s) + 2HCl(aq) -> CaCl2(aq) + CO2(g) + H2O(l)
 assuming chemical reactions at equilibrium.
 
-Last revision: 20/02/2026
+Last revision: 10/04/2026
 """
 
 import time
@@ -51,10 +51,8 @@
 n_species = len(species)  # Number of species
 
 # Initialize concentration vectors
-cc = np.zeros(n_cells * n_comps, dtype=np.float64)
-cs = np.zeros(n_cells * n_species, dtype=np.float64)
-phr.rm_get_concentrations(cc)
-phr.rm_get_species_concentrations(cs)
+cc = phr.rm.GetConcentrations()
+cs = phr.rm.GetSpeciesConcentrations()
 
 # Set HCl concentrations
 hcl = np.linspace(hcl_range[0], hcl_range[1], n_cells)  # mol/L
@@ -64,18 +62,18 @@
 indx_h = np.where(species == "H+")[0][0]
 
 # Update species concentrations with HCl
-cs_r = cs.reshape(n_species, n_cells).T
+cs_r = np.array(cs).reshape(n_species, n_cells).T
 cs_r[:, indx_cl] += hcl  # Cl-
 cs_r[:, indx_h] += hcl  # H+
 cs1 = cs_r.T.reshape(n_cells * n_species)
 
 # Run simulation with added HCl
-phr.rm_species_concentrations2_module(cs1)
-phr.rm_set_time(1.0)
-phr.rm_set_time_step(1.0)
+phr.rm.SpeciesConcentrations2Module(cs1)
+phr.rm.SetTime(1.0)
+phr.rm.SetTimeStep(1.0)
 
 start_time = time.time()
-phr.rm_run_cells()
+phr.rm.RunCells()
 elapsed = time.time() - start_time
 print(f"Simulation completed in {elapsed:.2f} seconds")
 
diff --git a/examples/ex2-BTEX_dissolution.ipynb b/examples/ex2-BTEX_dissolution.ipynb
index fdcf44a..90ff44d 100644
--- a/examples/ex2-BTEX_dissolution.ipynb
+++ b/examples/ex2-BTEX_dissolution.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,10 +147,8 @@
     "n_species = len(phr.species)\n",
     "\n",
     "# Get initial concentrations\n",
-    "cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "cs = np.zeros(n_cells * n_species, dtype=np.float64)\n",
-    "phr.rm_get_concentrations(cc)\n",
-    "phr.rm_get_species_concentrations(cs)\n",
+    "cc = np.array(phr.rm.GetConcentrations())\n",
+    "cs = np.array(phr.rm.GetSpeciesConcentrations())\n",
     "\n",
     "# Time step setup\n",
     "dt = sim_duration / n_steps * 24 * 3600.0  # Convert days to seconds\n",
@@ -179,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -197,12 +195,12 @@
     "    time_vector[step] = current_time\n",
     "\n",
     "    # Run simulation step\n",
-    "    phr.rm_set_time(current_time)\n",
-    "    phr.rm_set_time_step(dt)\n",
-    "    status = phr.rm_run_cells()\n",
+    "    phr.rm.SetTime(current_time)\n",
+    "    phr.rm.SetTimeStep(dt)\n",
+    "    phr.rm.RunCells()\n",
     "\n",
     "    # Store results\n",
-    "    status = phr.rm_get_concentrations(cc)\n",
+    "    cc = np.array(phr.rm.GetConcentrations())\n",
     "    concentration_results[step, :] = cc[component_map]\n",
     "\n",
     "elapsed = time.time() - start_time\n",
@@ -218,12 +216,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 2 Axes>"
       ]
diff --git a/examples/ex2-BTEX_dissolution.py b/examples/ex2-BTEX_dissolution.py
index 2380014..3b25ad5 100644
--- a/examples/ex2-BTEX_dissolution.py
+++ b/examples/ex2-BTEX_dissolution.py
@@ -3,7 +3,7 @@
 
 Models the dissolution of benzene and ethylbenzene from NAPL phase into aqueous phase.
 
-Last revision: 20/02/2026
+Last revision: 10/04/2026
 """
 
 import time
@@ -54,10 +54,8 @@
 n_species = len(species)
 
 # Get initial concentrations
-cc = np.zeros(n_cells * n_comps, dtype=np.float64)
-cs = np.zeros(n_cells * n_species, dtype=np.float64)
-phr.rm_get_concentrations(cc)
-phr.rm_get_species_concentrations(cs)
+cc = np.array(phr.rm.GetConcentrations())
+cs = np.array(phr.rm.GetSpeciesConcentrations())
 
 # Time step setup
 dt = sim_duration / n_steps * 24 * 3600.0  # Convert days to seconds
@@ -82,12 +80,12 @@
     time_vector[step] = current_time
 
     # Run simulation step
-    phr.rm_set_time(current_time)
-    phr.rm_set_time_step(dt)
-    status = phr.rm_run_cells()
+    phr.rm.SetTime(current_time)
+    phr.rm.SetTimeStep(dt)
+    phr.rm.RunCells()
 
     # Store results
-    status = phr.rm_get_concentrations(cc)
+    cc = np.array(phr.rm.GetConcentrations())
     concentration_results[step, :] = cc[component_map]
 
 elapsed = time.time() - start_time
diff --git a/examples/ex3-BTEX_dissolution_transport_coupling.ipynb b/examples/ex3-BTEX_dissolution_transport_coupling.ipynb
index 72584af..39f8b47 100644
--- a/examples/ex3-BTEX_dissolution_transport_coupling.ipynb
+++ b/examples/ex3-BTEX_dissolution_transport_coupling.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -122,8 +122,7 @@
     "    n_comps = len(components)\n",
     "\n",
     "    # Get initial concentrations\n",
-    "    cc = np.zeros(n_cells * n_comps, dtype=np.float64)\n",
-    "    phr.rm_get_concentrations(cc)\n",
+    "    cc = np.array(phr.rm.GetConcentrations())\n",
     "\n",
     "    # Prepare monitoring\n",
     "    monitored_species = [\"Benz\", \"Ethyl\"]\n",
@@ -153,10 +152,10 @@
     "        time_vector[step] = current_time\n",
     "\n",
     "        # 1) Reactive step\n",
-    "        phr.rm_set_time(current_time * 3600 * 24)  # Convert to seconds\n",
-    "        phr.rm_set_time_step(dt * 3600 * 24)\n",
-    "        phr.rm_run_cells()\n",
-    "        phr.rm_get_concentrations(cc)\n",
+    "        phr.rm.SetTime(current_time * 3600 * 24)  # Convert to seconds\n",
+    "        phr.rm.SetTimeStep(dt * 3600 * 24)\n",
+    "        phr.rm.RunCells()\n",
+    "        cc = np.array(phr.rm.GetConcentrations())\n",
     "        conc_matrix = cc.reshape((n_comps, n_cells)).T\n",
     "\n",
     "        # 2) Transport step\n",
@@ -176,7 +175,7 @@
     "\n",
     "        # Update concentrations in PhreeqcRM\n",
     "        cc = conc_matrix.T.flatten()\n",
-    "        phr.rm_set_concentrations(cc)\n",
+    "        phr.rm.SetConcentrations(cc)\n",
     "\n",
     "    elapsed = time.time() - start_time\n",
     "    print(f\"Simulation completed in {elapsed:.2f} seconds\")\n",
@@ -285,15 +284,15 @@
      "output_type": "stream",
      "text": [
       "Running equilibrium dissolution simulation...\n",
-      "Simulation completed in 13.72 seconds\n",
+      "Simulation completed in 15.50 seconds\n",
       "\n",
       "Running kinetic dissolution simulation...\n",
-      "Simulation completed in 13.89 seconds\n"
+      "Simulation completed in 15.55 seconds\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
diff --git a/examples/ex3-BTEX_dissolution_transport_coupling.py b/examples/ex3-BTEX_dissolution_transport_coupling.py
index 02333e8..d281514 100644
--- a/examples/ex3-BTEX_dissolution_transport_coupling.py
+++ b/examples/ex3-BTEX_dissolution_transport_coupling.py
@@ -9,7 +9,7 @@
 - Equilibrium dissolution (NAPL as equilibrium phases)
 - Kinetic dissolution (NAPL kinetic dissolution)
 
-Last revision: 20/02/2026
+Last revision: 10/04/2026
 """
 
 import time
@@ -32,9 +32,9 @@
 dt = 0.1  # Coupling time step (days)
 domain_length = 100.0  # Length of domain (m)
 n_threads = 6  # Threads for calculation (-1 for all CPUs)
-dispersivity = 0.05  # Dispersivity (m²)
+dispersivity = 0.05  # Dispersivity (m2)
 velocity = 1.0  # Groundwater velocity (m/d)
-diffusion_coeff = 0.0  # Molecular diffusion (m²/s)
+diffusion_coeff = 0.0  # Molecular diffusion (m2/s)
 sim_duration = 100.0  # Simulation duration (days)
 
 # Physical properties
@@ -47,7 +47,7 @@
 probe_location = 49.75  # Probe location (m) for results extraction
 
 # Derived parameters
-dispersion_coeff = dispersivity * velocity  # Dispersion coefficient (m²/d)
+dispersion_coeff = dispersivity * velocity  # Dispersion coefficient (m2/d)
 n_steps = int(sim_duration / dt)  # Number of time steps
 contaminant_spot = int(0.5 * n_cells / domain_length)  # Contaminant spot size
 
@@ -90,8 +90,7 @@ def run_simulation(pqi_file, kinetic=False):
     n_comps = len(components)
 
     # Get initial concentrations
-    cc = np.zeros(n_cells * n_comps, dtype=np.float64)
-    phr.rm_get_concentrations(cc)
+    cc = np.array(phr.rm.GetConcentrations())
 
     # Prepare monitoring
     monitored_species = ["Benz", "Ethyl"]
@@ -121,10 +120,10 @@ def run_simulation(pqi_file, kinetic=False):
         time_vector[step] = current_time
 
         # 1) Reactive step
-        phr.rm_set_time(current_time * 3600 * 24)  # Convert to seconds
-        phr.rm_set_time_step(dt * 3600 * 24)
-        phr.rm_run_cells()
-        phr.rm_get_concentrations(cc)
+        phr.rm.SetTime(current_time * 3600 * 24)  # Convert to seconds
+        phr.rm.SetTimeStep(dt * 3600 * 24)
+        phr.rm.RunCells()
+        cc = np.array(phr.rm.GetConcentrations())
         conc_matrix = cc.reshape((n_comps, n_cells)).T
 
         # 2) Transport step
@@ -144,7 +143,7 @@ def run_simulation(pqi_file, kinetic=False):
 
         # Update concentrations in PhreeqcRM
         cc = conc_matrix.T.flatten()
-        phr.rm_set_concentrations(cc)
+        phr.rm.SetConcentrations(cc)
 
     elapsed = time.time() - start_time
     print(f"Simulation completed in {elapsed:.2f} seconds")
diff --git a/mibiremo/irmresult.py b/mibiremo/irmresult.py
deleted file mode 100644
index a3657d4..0000000
--- a/mibiremo/irmresult.py
+++ /dev/null
@@ -1,138 +0,0 @@
-"""PhreeqcRM result code mapping and error handling utilities.
-
-This module provides utilities for interpreting and handling result codes returned
-by PhreeqcRM operations. PhreeqcRM functions return integer status codes that
-indicate success, failure, or specific error conditions. This module maps these
-numeric codes to human-readable error messages for improved debugging and logging.
-
-The result codes follow the PhreeqcRM C++ library conventions and correspond
-to the IrmResult enumeration defined in IrmResult.h of the original PhreeqcRM
-source code.
-
-Classes:
-    IRMStatus: Named tuple containing status code, name, and message with convenience methods.
-
-Functions:
-    irm_result: Maps integer error codes to IRMStatus objects with enhanced functionality.
-
-References:
-
-- [PhreeqcRM IrmResult.h File Reference](https://usgs-coupled.github.io/phreeqcrm/IrmResult_8h.html)
-
-"""
-
-from typing import NamedTuple
-
-
-class IRMStatus(NamedTuple):
-    """PhreeqcRM status result with enhanced functionality.
-
-    This named tuple extends the basic error code mapping with convenience methods
-    for better error handling and user experience.
-
-    Attributes:
-        code (int): The raw integer error code from PhreeqcRM.
-        name (str): Symbolic name of the error code (e.g., "IRM_OK", "IRM_FAIL").
-        message (str): Human-readable description of the error.
-    """
-
-    code: int
-    name: str
-    message: str
-
-    def __bool__(self) -> bool:
-        """Return True if the operation was successful (code == 0).
-
-        Examples:
-            >>> result = rm.rm_run_cells()
-            >>> if result:
-            >>>     print("Success!")
-            >>> else:
-            >>>     print(f"Error: {result}")
-        """
-        return self.code == 0
-
-    def __int__(self) -> int:
-        """Return the raw integer code for backwards compatibility.
-
-        Examples:
-            >>> result = rm.rm_run_cells()
-            >>> if int(result) == 0:  # Still works
-            >>>     print("Success!")
-        """
-        return self.code
-
-    def __str__(self) -> str:
-        """Return a formatted string representation of the status.
-
-        Returns:
-            str: Formatted string in the form "ERROR_NAME: Error description"
-        """
-        return f"{self.name}: {self.message}"
-
-    def raise_for_status(self, context: str = "") -> None:
-        """Raise an exception if the operation failed.
-
-        Args:
-            context (str, optional): Additional context for the error message.
-
-        Raises:
-            RuntimeError: If the status code indicates failure (non-zero).
-
-        Examples:
-            >>> result = rm.rm_load_database("invalid.dat")
-            >>> result.raise_for_status("Loading database")
-            RuntimeError: Loading database: IRM_FAIL: Failure, Unspecified
-        """
-
-        if not self:
-            prefix = f"{context}: " if context else ""
-            raise RuntimeError(f"{prefix}{self}")
-
-
-def irm_result(code: int) -> IRMStatus:
-    """Map PhreeqcRM integer error codes to enhanced status objects.
-
-    Args:
-        code (int): Integer error code returned by PhreeqcRM functions.
-            Return codes are listed below:
-            - 0: Success (IRM_OK)
-            - -1: Out of memory error
-            - -2: Invalid variable type
-            - -3: Invalid argument
-            - -4: Invalid row index
-            - -5: Invalid column index
-            - -6: Invalid PhreeqcRM instance ID
-            - -7: Unspecified failure
-
-    Returns:
-        IRMStatus: A named tuple containing:
-            - code (int): The raw integer error code
-            - name (str): Symbolic error code name (e.g., "IRM_OK", "IRM_FAIL")
-            - message (str): Human-readable error description
-
-    Examples:
-        >>> result = irm_result(0)
-        >>> print(result)  # "IRM_OK: Success"
-        >>> if result:  # True for success
-        >>>     print("Operation successful")
-        >>>
-        >>> error = irm_result(-1)
-        >>> print(int(error))  # -1 (backwards compatibility)
-        >>> error.raise_for_status("Memory allocation")  # Raises RuntimeError
-    """
-    mapping = {
-        0: ("IRM_OK", "Success"),
-        -1: ("IRM_OUTOFMEMORY", "Failure, Out of memory"),
-        -2: ("IRM_BADVARTYPE", "Failure, Invalid VAR type"),
-        -3: ("IRM_INVALIDARG", "Failure, Invalid argument"),
-        -4: ("IRM_INVALIDROW", "Failure, Invalid row"),
-        -5: ("IRM_INVALIDCOL", "Failure, Invalid column"),
-        -6: ("IRM_BADINSTANCE", "Failure, Invalid rm instance id"),
-        -7: ("IRM_FAIL", "Failure, Unspecified"),
-    }
-
-    if code in mapping:
-        name, message = mapping[code]
-        return IRMStatus(code, name, message)
-    return IRMStatus(code, "IRM_UNKNOWN", f"Unknown error code: {code}")
diff --git a/mibiremo/lib/PhreeqcRM.dll b/mibiremo/lib/PhreeqcRM.dll
deleted file mode 100644
index 1c49811..0000000
Binary files a/mibiremo/lib/PhreeqcRM.dll and /dev/null differ
diff --git a/mibiremo/lib/PhreeqcRM.so b/mibiremo/lib/PhreeqcRM.so
deleted file mode 100644
index 868b884..0000000
Binary files a/mibiremo/lib/PhreeqcRM.so and /dev/null differ
diff --git a/mibiremo/lib/README.md b/mibiremo/lib/README.md
deleted file mode 100644
index e80b297..0000000
--- a/mibiremo/lib/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# MiBiReMo (Microbiome Bioremediation Reaction Module) - PhreeqcRM-Python Interface - Library files
-
-## Versions
-- Windows 64-bit : PhreeqcRM 3.7.3 15968. Compiler: Visual Studio Community 2019.
-- Linux 64-bit : PhreeqcRM 3.7.3 15968. Compiler: gcc 11.4.0.
-
-
-
-## References
-- [README.TXT](https://water.usgs.gov/water-resources/software/PHREEQC/PhreeqcRM_ReadMe.txt) - USGS Official Readme for compiling PhreeqcRM
-- [PhreeqcRM Library Documentation](
-https://water.usgs.gov/water-resources/software/PHREEQC/documentation/phreeqcrm-html/index.html) - USGS Official C/C++ library Documentation for PhreeqcRM
diff --git a/mibiremo/phreeqc.py b/mibiremo/phreeqc.py
index 0266baa..e87b3b5 100644
--- a/mibiremo/phreeqc.py
+++ b/mibiremo/phreeqc.py
@@ -1,35 +1,34 @@
-"""Python interface to PhreeqcRM for geochemical reactive transport modeling.
+"""Python interface to PhreeqcRM for biogeochemical reactive transport modeling.
 
 PhreeqcRM is a reaction module developed by the U.S. Geological Survey (USGS)
-for coupling geochemical calculations with transport models. It provides a
-high-performance interface to PHREEQC geochemical modeling capabilities for
-reactive transport simulations in environmental and hydrological applications.
+for coupling biogeochemical calculations with transport models. It provides a
+high-performance interface to PHREEQC modeling capabilities for reactive transport
+simulations in environmental and hydrological applications.
 
 PhreeqcRM enables:
 
-- Multi-threaded geochemical calculations for large-scale transport models
-- Equilibrium and kinetic geochemical reactions in porous media
+- Multi-threaded biogeochemical calculations for large-scale transport models
+- Equilibrium and kinetic biogeochemical reactions in porous media
 - Parallel processing for computationally intensive reactive transport
 
-This interface provides a Python wrapper around the PhreeqcRM C library,
-simplifying the process of integrating geochemical calculations with transport models.
-
-All rm_* methods in this class correspond directly to PhreeqcRM C functions, converted to
-lowercase Python naming convention with underscores (e.g., RM_LoadDatabase -> rm_load_database).
+This interface wraps the ``phreeqcrm`` pip package, exposing four high-level
+convenience methods (``create``, ``initialize_phreeqc``,
+``run_initial_from_file``, ``get_selected_output_df``) and a public
+``self.rm`` attribute (the underlying ``phreeqcrm.PhreeqcRM`` instance) for
+advanced users who need direct access to the full PhreeqcRM API.
 
 PhreeqcRM documentation and source code can be found at:
 
 - [PhreeqcRM Documentation](https://usgs-coupled.github.io/phreeqcrm/namespacephreeqcrm.html)
 - [PhreeqcRM GitHub Repository](https://github.com/usgs-coupled/phreeqcrm)
 
-Last revision: 17/02/2026
+Last revision: 10/04/2026
 """
 
-import ctypes
 import os
 import numpy as np
 import pandas as pd
-from .irmresult import irm_result, IRMStatus
+import phreeqcrm
 
 
 class PhreeqcRM:
@@ -54,25 +53,20 @@ class PhreeqcRM:
         2. Load thermodynamic database with initialize_phreeqc()
         3. Set initial conditions with run_initial_from_file()
         4. In transport time loop:
-           - Transfer concentrations to reaction module with rm_set_concentrations()
-           - Advance time with rm_set_time() and rm_set_time_step()
-           - Run reactions with rm_run_cells()
-           - Retrieve updated concentrations with rm_get_concentrations()
+           - Transfer concentrations to reaction module with rm.SetConcentrations()
+           - Advance time with rm.SetTime() and rm.SetTimeStep()
+           - Run reactions with rm.RunCells()
+           - Retrieve updated concentrations with rm.GetConcentrations()
 
     Attributes:
-        dllpath (str): Path to the PhreeqcRM dynamic library file.
         nxyz (int): Number of grid cells in the reactive transport model.
         n_threads (int): Number of threads for parallel geochemical processing.
-        libc (ctypes.CDLL): Handle to the loaded PhreeqcRM dynamic library.
-        id (int): Unique instance identifier returned by RM_Create.
+        rm (phreeqcrm.PhreeqcRM): The underlying PhreeqcRM instance. Use this
+            attribute to access the full PhreeqcRM API directly (e.g.,
+            ``rm.SetTime(t)``, ``rm.GetConcentrations()``).
         components (numpy.ndarray): Array of component names for transport.
         species (numpy.ndarray): Array of aqueous species names in the system.
 
-    Note:
-        This interface requires the PhreeqcRM dynamic library to be available
-        in the lib/ subdirectory. The library handles the underlying PHREEQC
-        calculations and memory management.
-
     Examples:
         See page [Examples](examples.md) for usage examples.
     """
@@ -84,61 +78,42 @@ def __init__(self):
         created using the create() method before it can be used for calculations.
         """
         self._initialized = False
-        self.dllpath = None
         self.nxyz = 1
         self.n_threads = 1
-        self.libc = None
-        self.id = None
+        self.rm = None
         self.components = None
         self.species = None
 
-    def create(self, dllpath=None, nxyz=1, n_threads=1) -> None:
+    def create(self, nxyz=1, n_threads=1) -> None:
         """Creates a PhreeqcRM reaction module instance.
 
-        Initializes the PhreeqcRM library, loads the dynamic library, and creates
-        a reaction module with the specified number of grid cells and threads.
+        Initializes the PhreeqcRM library and creates a reaction module with
+        the specified number of grid cells and threads.
         This method must be called before any other PhreeqcRM operations.
 
         Args:
-            dllpath (str, optional): Path to the PhreeqcRM library. If None,
-                uses the default library path based on the operating system.
-                Defaults to None.
             nxyz (int, optional): Number of grid cells in the model. Must be
                 positive. Defaults to 1.
             n_threads (int, optional): Number of threads for parallel processing.
                 Use -1 for automatic detection of CPU count. Defaults to 1.
 
         Raises:
-            Exception: If the operating system is not supported (Windows/Linux only).
             RuntimeError: If PhreeqcRM instance creation fails.
 
         Examples:
             >>> rm = PhreeqcRM()
             >>> rm.create(nxyz=100, n_threads=4)
         """
-        if dllpath is None:
-            # If no path is provided, use the default path, based on operating system
-            if os.name == "nt":
-                dllpath = os.path.join(os.path.dirname(__file__), "lib", "PhreeqcRM.dll")
-            elif os.name == "posix":
-                dllpath = os.path.join(os.path.dirname(__file__), "lib", "PhreeqcRM.so")
-            else:
-                msg = "Operating system not supported"
-                raise Exception(msg)
-        self.dllpath = dllpath
-
         if n_threads == -1:
             n_threads = os.cpu_count()
 
         self.n_threads = n_threads
         self.nxyz = nxyz
-        self.libc = ctypes.CDLL(dllpath)
         try:
-            self.id = self.libc.RM_Create(nxyz, n_threads)
+            self.rm = phreeqcrm.PhreeqcRM(nxyz, n_threads)
             self._initialized = True
         except Exception as e:
-            msg = f"Failed to create PhreeqcRM instance: {e}"
-            raise RuntimeError(msg)
+            raise RuntimeError(f"Failed to create PhreeqcRM instance: {e}")
 
     def initialize_phreeqc(
         self,
@@ -178,39 +153,28 @@ def initialize_phreeqc(
             >>> rm.create(nxyz=100)
             >>> rm.initialize_phreeqc("phreeqc.dat", units_solution=1)
         """
-
         if not self._initialized:
             raise RuntimeError("PhreeqcRM instance not initialized. Call create() first.")
 
-        # Load database
-        status = self.rm_load_database(database_path)
-        if not status:
-            raise RuntimeError(f"Failed to load Phreeqc database: {status}")
-
-        # Set properties/parameters
-        self.rm_set_component_h2o(0)  # Don't include H2O in component list
-        self.rm_set_rebalance_fraction(0.5)  # Rebalance thread load
-
-        # Set units
-        self.rm_set_units_solution(units_solution)
-        self.rm_set_units_p_passemblage(units)
-        self.rm_set_units_exchange(units)
-        self.rm_set_units_surface(units)
-        self.rm_set_units_gas_phase(units)
-        self.rm_set_units_ss_assemblage(units)
-        self.rm_set_units_kinetics(units)
-
-        # Set porosity and saturation
-        self.rm_set_porosity(porosity * np.ones(self.nxyz))
-        self.rm_set_saturation(saturation * np.ones(self.nxyz))
-
-        # Create error log files
-        self.rm_set_file_prefix("phr")
-        self.rm_open_files()
-
-        # Multicomponent diffusion settings
+        status = self.rm.LoadDatabase(database_path)
+        if status < 0:
+            raise RuntimeError(f"Failed to load Phreeqc database (error code: {status})")
+
+        self.rm.SetComponentH2O(False)
+        self.rm.SetRebalanceFraction(0.5)
+        self.rm.SetUnitsSolution(units_solution)
+        self.rm.SetUnitsPPassemblage(units)
+        self.rm.SetUnitsExchange(units)
+        self.rm.SetUnitsSurface(units)
+        self.rm.SetUnitsGasPhase(units)
+        self.rm.SetUnitsSSassemblage(units)
+        self.rm.SetUnitsKinetics(units)
+        self.rm.SetPorosity(porosity * np.ones(self.nxyz))
+        self.rm.SetSaturationUser(saturation * np.ones(self.nxyz))
+        self.rm.SetFilePrefix("phr")
+        self.rm.OpenFiles()
         if multicomponent:
-            self.rm_set_species_save_on(1)
+            self.rm.SetSpeciesSaveOn(True)
 
     def run_initial_from_file(self, pqi_file, ic):
         """Set up initial conditions from PHREEQC input file and initial conditions array.
@@ -244,50 +208,29 @@ def run_initial_from_file(self, pqi_file, ic):
             >>> ic = np.array([[1, -1, -1, -1, -1, -1, -1]])  # Only solution 1
             >>> rm.run_initial_from_file("initial.pqi", ic)
         """
+        status = self.rm.RunFile(True, True, True, pqi_file)
+        if status < 0:
+            raise RuntimeError(f"Failed to run Phreeqc input file (error code: {status})")
 
-        # Run the initial setup file
-        status = self.rm_run_file(1, 1, 1, pqi_file)
-        if not status:
-            raise RuntimeError(f"Failed to run Phreeqc input file: {status}")
-
-        # Check size of initial conditions array
         if ic.shape != (self.nxyz, 7):
             raise ValueError(f"Initial conditions array must have shape ({self.nxyz}, 7), got {ic.shape}")
 
-        # Be sure that ic is a numpy array
         if not isinstance(ic, np.ndarray):
             try:
                 ic = np.array(ic).astype(np.int32)
             except Exception as e:
                 raise ValueError("Initial conditions must be convertible to a numpy array of integers") from e
 
-        # Reshape initial conditions to 1D array
-        ic1 = np.reshape(ic.T, self.nxyz * 7).astype(np.int32)
-
-        # ic2 contains numbers for a second entity that mixes with the first entity (here, it is not used)
-        ic2 = -1 * np.ones(self.nxyz * 7, dtype=np.int32)
-
-        # f1 contains the fractions of the first entity in each cell (here, it is set to 1.0)
-        f1 = np.ones(self.nxyz * 7, dtype=np.float64)
+        ic1 = ic.flatten("F").astype(np.int32)
+        self.rm.InitialPhreeqc2Module(ic1)
 
-        status = self.rm_initial_phreeqc2_module(ic1, ic2, f1)
+        self.rm.FindComponents()
+        self.components = np.array(list(self.rm.GetComponents()))
+        self.species = np.array(list(self.rm.GetSpeciesNames()))
 
-        # Get component and species information
-        n_comps = self.rm_find_components()
-        n_species = self.rm_get_species_count()
-
-        self.components = np.zeros(n_comps, dtype="U20")
-        for i in range(n_comps):
-            self.rm_get_component(i, self.components, 20)
-
-        self.species = np.zeros(n_species, dtype="U20")
-        for i in range(n_species):
-            self.rm_get_species_name(i, self.species, 20)
-
-        # Run initial equilibrium step
-        self.rm_set_time(0.0)
-        self.rm_set_time_step(0.0)
-        status = self.rm_run_cells()
+        self.rm.SetTime(0.0)
+        self.rm.SetTimeStep(0.0)
+        self.rm.RunCells()
 
     def get_selected_output_df(self) -> pd.DataFrame:
         """Retrieve selected output data as a pandas DataFrame.
@@ -307,1367 +250,7 @@ def get_selected_output_df(self) -> pd.DataFrame:
             >>> print(df.columns)  # Show available output variables
             >>> print(df['pH'])     # Access pH values for all cells
         """
-        # Get selected ouput headings
-        ncolsel = self.rm_get_selected_output_column_count()
-        selout_h = np.zeros(ncolsel, dtype="U100")
-        for i in range(ncolsel):
-            self.rm_get_selected_output_heading(i, selout_h, 100)
-        so = np.zeros(ncolsel * self.nxyz).reshape(self.nxyz, ncolsel)
-        self.rm_get_selected_output(so)
-        return pd.DataFrame(so.reshape(ncolsel, self.nxyz).T, columns=selout_h)
-
-    ### PhreeqcRM functions
-    def rm_abort(self, result, err_str) -> IRMStatus:
-        """Abort the PhreeqcRM run.
-
-        Args:
-            result (int): Error code indicating reason for abort.
-            err_str (str): Error message string.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_Abort(self.id, result, err_str))
-
-    def rm_close_files(self) -> IRMStatus:
-        """Close output files opened by RM_OpenFiles.
-
-        Closes all output files that were opened by RM_OpenFiles, including
-        error logs and debug files. This method should be called before
-        destroying the PhreeqcRM instance to ensure proper file cleanup.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Examples:
-            >>> rm.rm_open_files()  # Open files for logging
-            >>> # ... perform calculations ...
-            >>> result = rm.rm_close_files()  # Close files when done
-            >>> if not result:
-            >>>     print(f"Warning: {result}")
-        """
-        return irm_result(self.libc.RM_CloseFiles(self.id))
-
-    def rm_concentrations2utility(self, c, n, tc, p_atm) -> IRMStatus:
-        """Transfer concentrations from a cell to the utility IPhreeqc instance.
-
-        Transfers solution concentrations from a reaction cell to the utility
-        IPhreeqc instance for further calculations or analysis. This method
-        allows access to the full PHREEQC functionality for individual cells.
-
-        Args:
-            c (numpy.ndarray): Array of component concentrations to transfer.
-            n (int): Cell number from which to transfer concentrations.
-            tc (float): Temperature in Celsius for the utility calculation.
-            p_atm (float): Pressure in atmospheres for the utility calculation.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_Concentrations2Utility(self.id, c, n, tc, p_atm))
-
-    def rm_create_mapping(self, grid2chem) -> IRMStatus:
-        """Create a mapping from grid cells to reaction cells.
-
-        Establishes the relationship between transport grid cells and reaction
-        cells, allowing for optimization when multiple grid cells share the
-        same chemical composition. This can significantly reduce computational
-        requirements for large models with repeating chemical conditions.
-
-        Args:
-            grid2chem (numpy.ndarray): Array mapping grid cells to reaction cells.
-                Length should equal the number of grid cells in the transport model.
-                Values are indices of reaction cells (0-based).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            After calling this method, the number of reaction cells may be
-            different from the number of grid cells, potentially reducing
-            computational overhead.
-        """
-        return irm_result(self.libc.RM_CreateMapping(self.id, grid2chem.ctypes))
-
-    def rm_decode_error(self, e) -> str:
-        """Decode error code to human-readable message.
-
-        Converts a numeric error code returned by PhreeqcRM functions into
-        a descriptive error message string for debugging and logging purposes.
-
-        Args:
-            e (int): Error code to decode.
-
-        Returns:
-            str: Human-readable error message corresponding to the error code.
-
-        Examples:
-            >>> result = rm.rm_run_cells()
-            >>> if not result:
-            >>>     error_msg = rm.rm_decode_error(result.code)
-            >>>     print(f"Error: {error_msg}")
-        """
-        return self.libc.RM_DecodeError(self.id, e)
-
-    def rm_destroy(self) -> IRMStatus:
-        """Destroy a PhreeqcRM instance and free all associated memory.
-
-        Deallocates all memory associated with the PhreeqcRM instance, including
-        reaction cells, species data, and internal data structures. This method
-        should be called when the PhreeqcRM instance is no longer needed to
-        prevent memory leaks.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Warning:
-            After calling this method, the PhreeqcRM instance should not be
-            used for any further operations. All method calls will fail.
-
-        Examples:
-            >>> rm = PhreeqcRM()
-            >>> rm.create(nxyz=100)
-            >>> # ... use PhreeqcRM instance ...
-            >>> rm.rm_destroy()  # Clean up when finished
-        """
-        return irm_result(self.libc.RM_Destroy(self.id))
-
-    def rm_dump_module(self, dump_on, append) -> IRMStatus:
-        """Enable or disable dumping of reaction module data to file.
-        Controls the output of detailed reaction module data to a dump file
-        for debugging and analysis purposes. The dump file contains complete
-        information about the state of all reaction cells.
-
-        Args:
-            dump_on (int): Enable (1) or disable (0) dump file creation.
-            append (int): Append to existing file (1) or overwrite (0).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            The dump file name is set by RM_SetDumpFileName(). If no name
-            is set, a default name will be used.
-        """
-        return irm_result(self.libc.RM_DumpModule(self.id, dump_on, append))
-
-    def rm_error_message(self, errstr) -> IRMStatus:
-        """Print an error message to the error output file.
-
-        Writes an error message to the PhreeqcRM error log file. The message
-        is formatted with timestamp and process information for debugging.
-
-        Args:
-            errstr (str): Error message string to write to the log.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Error logging must be enabled with RM_OpenFiles() for messages
-            to be written to file.
-        """
-        return irm_result(self.libc.RM_ErrorMessage(self.id, errstr))
-
-    def rm_find_components(self) -> int:
-        """Find and count components for transport calculations.
-
-        Analyzes all chemical definitions in the reaction module to determine
-        the minimum set of components (elements plus charge) required for
-        transport calculations. This method must be called after initial
-        conditions are set but before starting transport calculations.
-
-        The components identified include:
-            - Chemical elements present in the system
-            - Electric charge balance
-            - Isotopes if defined in the database
-
-        Returns:
-            int: Number of components required for transport calculations.
-                This defines the number of concentrations that must be
-                transported for each grid cell.
-
-        Note:
-            This method should be called after RM_InitialPhreeqc2Module()
-            and before beginning transport time stepping. The returned
-            count determines array sizes for concentration transfers.
-
-        Examples:
-            >>> rm.run_initial_from_file("initial.pqi", ic_array)
-            >>> ncomp = rm.rm_find_components()
-            >>> print(f"Transport requires {ncomp} components")
-        """
-        return self.libc.RM_FindComponents(self.id)
-
-    def rm_get_backward_mapping(self, n, list, size) -> IRMStatus:
-        """Get backward mapping from reaction cells to grid cells.
-
-        Retrieves the list of grid cell numbers that map to a specific
-        reaction cell. This is the inverse of the forward mapping and is
-        useful for distributing reaction cell results back to grid cells.
-
-        Args:
-            n (int): Reaction cell number for which to get the mapping.
-            list (numpy.ndarray): Array to receive grid cell numbers that
-                map to the specified reaction cell.
-            size (int): Size of the list array.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-                The number of grid cells mapping to reaction cell n.
-        """
-        return irm_result(self.libc.RM_GetBackwardMapping(self.id, n, list, size))
-
-    def rm_get_chemistry_cell_count(self) -> int:
-        """Get the number of reaction cells in the module.
-
-        Returns the number of reaction cells currently defined in the
-        PhreeqcRM instance. This may be different from the number of
-        grid cells if a mapping has been created to reduce computational
-        requirements by grouping cells with identical chemistry.
-
-        Returns:
-            int: Number of reaction cells in the module.
-
-        Note:
-            Without cell mapping, this equals the number of grid cells.
-            With mapping, this may be significantly smaller, improving
-            computational efficiency.
-        """
-        return self.libc.RM_GetChemistryCellCount(self.id)
-
-    def rm_get_component(self, num, chem_name, length) -> IRMStatus:
-        """Get the name of a component by index.
-
-        Retrieves the name of a transport component identified by its index.
-        Components are the chemical entities that must be transported in
-        reactive transport simulations, typically elements plus charge.
-
-        Args:
-            num (int): Index of the component (0-based).
-            chem_name (numpy.ndarray): String array to store component names.
-                The name will be stored at index num.
-            length (int): Maximum length of the component name string.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            This method is typically used in a loop to retrieve all component
-            names after calling RM_FindComponents().
-        """
-        string = ctypes.create_string_buffer(length)
-        status = self.libc.RM_GetComponent(self.id, num, string, length)
-        chem_name[num] = string.value.decode()
-        return irm_result(status)
-
-    def rm_get_concentrations(self, c) -> IRMStatus:
-        """Retrieve component concentrations from reaction cells.
-
-        Extracts current component concentrations from all reaction cells
-        after geochemical calculations. These concentrations represent the
-        dissolved components that must be transported in reactive transport
-        simulations.
-
-        Args:
-            c (numpy.ndarray): Array to receive concentrations with shape
-                (nxyz * ncomps). Will be filled with current concentrations
-                in the units specified by RM_SetUnitsSolution().
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            The array is organized with all components for cell 0, followed
-            by all components for cell 1, etc. Use this method after
-            RM_RunCells() to get updated concentrations for transport.
-
-        Examples:
-            >>> ncomp = rm.rm_get_component_count()
-            >>> conc = np.zeros(nxyz * ncomp)
-            >>> result = rm.rm_get_concentrations(conc)
-            >>> if result:
-            >>>     # Reshape to (nxyz, ncomp) for easier handling
-            >>>     conc_2d = conc.reshape(nxyz, ncomp)
-        """
-        return irm_result(self.libc.RM_GetConcentrations(self.id, c.ctypes))
-
-    def rm_get_density(self, density) -> IRMStatus:
-        """Get solution density for all reaction cells.
-
-        Retrieves the calculated solution density for each reaction cell
-        based on the current chemical composition, temperature, and pressure.
-        Densities are calculated using the thermodynamic database properties.
-
-        Args:
-            density (numpy.ndarray): Array to receive density values with
-                length equal to the number of reaction cells. Units are kg/L.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Density calculations depend on the thermodynamic database and
-            the specific solution composition. This method should be called
-            after RM_RunCells() to get current density values.
-        """
-        return irm_result(self.libc.RM_GetDensity(self.id, density.ctypes))
-
-    def rm_get_end_cell(self, ec) -> IRMStatus:
-        """Get the ending cell number for the current MPI process.
-
-        In parallel (MPI) calculations, each process handles a subset of
-        reaction cells. This method returns the index of the last cell
-        handled by the current MPI process.
-
-        Args:
-            ec (ctypes pointer): Pointer to receive the ending cell index.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            For single-process calculations, this typically returns nxyz-1.
-            For MPI calculations, the range [start_cell, end_cell] defines
-            the cells handled by the current process.
-        """
-        return irm_result(self.libc.RM_GetEndCell(self.id, ec))
-
-    def rm_get_equilibrium_phase_count(self) -> int:
-        """Get the number of equilibrium phases defined in the system.
-
-        Returns the count of mineral phases that can potentially precipitate
-        or dissolve based on the thermodynamic database and initial conditions
-        defined in the PhreeqcRM instance.
-
-        Returns:
-            int: Number of equilibrium phases defined in the system.
-
-        Note:
-            This count includes all phases that have been referenced in
-            EQUILIBRIUM_PHASES blocks in the initial conditions, regardless
-            of whether they are currently present in any cells.
-        """
-        return self.libc.RM_GetEquilibriumPhaseCount(self.id)
-
-    def rm_get_equilibrium_phase_name(self, num, name, l1) -> IRMStatus:
-        """Get the name of an equilibrium phase by index.
-
-        Retrieves the name of a mineral phase that can precipitate or dissolve
-        in the geochemical system, identified by its index.
-
-        Args:
-            num (int): Index of the equilibrium phase (0-based).
-            name (ctypes pointer): Buffer to receive the phase name.
-            l1 (int): Maximum length of the name buffer.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_GetEquilibriumPhaseName(self.id, num, name, l1))
-
-    def rm_get_error_string(self, errstr, length) -> IRMStatus:
-        """Get the current error string from PhreeqcRM.
-
-        Retrieves the most recent error message generated by PhreeqcRM
-        operations. This provides detailed information about the last
-        error that occurred during calculations.
-
-        Args:
-            errstr (ctypes pointer): Buffer to receive the error string.
-            length (int): Maximum length of the error string buffer.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_GetErrorString(self.id, errstr, length))
-
-    def rm_get_error_string_length(self) -> int:
-        """Get the length of the current error string.
-
-        Returns the length of the error message string that can be retrieved
-        with RM_GetErrorString(). Use this to allocate appropriate buffer
-        size before calling RM_GetErrorString().
-
-        Returns:
-            int: Length of the current error string in characters.
-        """
-        return self.libc.RM_GetErrorStringLength(self.id)
-
-    def rm_get_exchange_name(self, num, name, l1):
-        return self.libc.RM_GetExchangeName(self.id, num, name, l1)
-
-    def rm_get_exchange_species_count(self):
-        return self.libc.RM_GetExchangeSpeciesCount(self.id)
-
-    def rm_get_exchange_species_name(self, num, name, l1):
-        return self.libc.RM_GetExchangeSpeciesName(self.id, num, name, l1)
-
-    def rm_get_file_prefix(self, prefix, length):
-        return self.libc.RM_GetFilePrefix(self.id, prefix.encode(), length)
-
-    def rm_get_gas_components_count(self):
-        return self.libc.RM_GetGasComponentsCount(self.id)
-
-    def rm_get_gas_components_name(self, nun, name, l1):
-        return self.libc.RM_GetGasComponentsName(self.id, nun, name, l1)
-
-    def rm_get_gfw(self, gfw) -> IRMStatus:
-        """Get gram formula weights for transport components.
-
-        Retrieves the gram formula weights (molecular weights) for all
-        transport components. These weights are used to convert between
-        molar and mass-based concentrations in transport calculations.
-
-        Args:
-            gfw (numpy.ndarray): Array to receive gram formula weights.
-                Length should equal the number of components. Units are g/mol.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            The gram formula weights correspond to the components identified
-            by RM_FindComponents() and can be retrieved by RM_GetComponent().
-        """
-        return irm_result(self.libc.RM_GetGfw(self.id, gfw.ctypes))
-
-    def rm_get_grid_cell_count(self) -> int:
-        """Get the number of grid cells in the model.
-
-        Returns the total number of grid cells defined for the transport
-        model, as specified when the PhreeqcRM instance was created.
-
-        Returns:
-            int: Number of grid cells in the transport model.
-
-        Note:
-            This is the nxyz parameter that was passed to RM_Create().
-            It represents the total number of cells in the transport grid,
-            which may be different from the number of reaction cells if
-            cell mapping is used.
-        """
-        return self.libc.RM_GetGridCellCount(self.id)
-
-    def rm_get_iphreeqc_id(self, i):
-        return self.libc.RM_GetIPhreeqcId(self.id, i)
-
-    def rm_get_kinetic_reactions_count(self):
-        return self.libc.RM_GetKineticReactionsCount(self.id)
-
-    def rm_get_kinetic_reactions_name(self, num, name, l1):
-        return self.libc.RM_GetKineticReactionsName(self.id, num, name, l1)
-
-    def rm_get_mpi_myself(self):
-        return self.libc.RM_GetMpiMyself(self.id)
-
-    def rm_get_mpi_tasks(self):
-        return self.libc.RM_GetMpiTasks(self.id)
-
-    def rm_get_nth_selected_output_user_number(self, n):
-        return self.libc.RM_GetNthSelectedOutputUserNumber(self.id, n)
-
-    def rm_get_saturation(self, sat_calc) -> IRMStatus:
-        """Get saturation values for all reaction cells.
-
-        Retrieves the current saturation values for all reaction cells.
-        Saturation represents the fraction of pore space occupied by water
-        and affects the volume calculations in reactive transport.
-
-        Args:
-            sat_calc (numpy.ndarray): Array to receive saturation values.
-                Length should equal the number of reaction cells.
-                Values range from 0 to 1.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_GetSaturation(self.id, sat_calc.ctypes))
-
-    def rm_get_selected_output(self, so) -> IRMStatus:
-        """Retrieve selected output data from all reaction cells.
-
-        Extracts the current selected output data, which includes calculated
-        properties such as pH, pe, ionic strength, mineral saturation indices,
-        species activities, and user-defined calculations specified in the
-        PHREEQC input files.
-
-        Args:
-            so (numpy.ndarray): Array to receive selected output data with
-                shape (nxyz, ncol) where ncol is the number of selected
-                output columns defined in the PHREEQC input.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Use RM_GetSelectedOutputColumnCount() to determine the number
-            of columns and RM_GetSelectedOutputHeading() to get column names.
-            The get_selected_output_df() method provides a more convenient pandas
-            DataFrame interface to this data.
-        """
-        return irm_result(self.libc.RM_GetSelectedOutput(self.id, so.ctypes))
-
-    def rm_get_nth_selected_output_column_count(self):
-        return self.libc.RM_GetNthSelectedOutputColumnCount(self.id)
-
-    def rm_get_selected_output_count(self):
-        return self.libc.RM_GetSelectedOutputCount(self.id)
-
-    def rm_get_selected_output_heading(self, col, headings, length):
-        string = ctypes.create_string_buffer(length)
-        status = self.libc.RM_GetSelectedOutputHeading(self.id, col, string, length)
-        headings[col] = string.value.decode()
-        return irm_result(status)
-
-    def rm_get_selected_output_column_count(self) -> int:
-        """Get number of columns in selected output.
-
-        Returns:
-            int: Number of selected output columns.
-        """
-        return self.libc.RM_GetSelectedOutputColumnCount(self.id)
-
-    def rm_get_selected_output_row_count(self) -> int:
-        """Get number of rows in selected output.
-
-        Returns:
-            int: Number of selected output rows.
-        """
-        return self.libc.RM_GetSelectedOutputRowCount(self.id)
-
-    def rm_get_si_count(self):
-        return self.libc.RM_GetSICount(self.id)
-
-    def rm_get_si_name(self, num, name, l1):
-        return self.libc.RM_GetSIName(self.id, num, name, l1)
-
-    def rm_get_solid_solution_components_count(self):
-        return self.libc.RM_GetSolidSolutionComponentsCount(self.id)
-
-    def rm_get_solid_solution_components_name(self, num, name, l1):
-        return self.libc.RM_GetSolidSolutionComponentsName(self.id, num, name, l1)
-
-    def rm_get_solid_solution_name(self, num, name, l1):
-        return self.libc.RM_GetSolidSolutionName(self.id, num, name, l1)
-
-    def rm_get_solution_volume(self, vol):
-        return self.libc.RM_GetSolutionVolume(self.id, vol.ctypes)
-
-    def rm_get_species_concentrations(self, species_conc) -> IRMStatus:
-        """Retrieve aqueous species concentrations from reaction cells.
-
-        Extracts the concentrations of individual aqueous species from all
-        reaction cells. This provides more detailed chemical information than
-        component concentrations, including the speciation of dissolved elements.
-
-        Args:
-            species_conc (numpy.ndarray): Array to receive species concentrations
-                with shape (nxyz * nspecies). Species concentrations are in
-                the same units as solution concentrations (mol/L, mmol/L, or μmol/L).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Species saving must be enabled with RM_SetSpeciesSaveOn(1) before
-            this method can be used. Use RM_GetSpeciesCount() to determine the
-            number of species and RM_GetSpeciesName() to get species names.
-
-        Examples:
-            >>> rm.rm_set_species_save_on(1)  # Enable species saving
-            >>> rm.rm_run_cells()  # Run reactions
-            >>> nspecies = rm.rm_get_species_count()
-            >>> species_c = np.zeros(nxyz * nspecies)
-            >>> rm.rm_get_species_concentrations(species_c)
-        """
-        return irm_result(self.libc.RM_GetSpeciesConcentrations(self.id, species_conc.ctypes))
-
-    def rm_get_species_count(self) -> int:
-        """Get the number of aqueous species in the geochemical system.
-
-        Returns the total number of dissolved aqueous species that can exist
-        in the current geochemical system based on the loaded thermodynamic
-        database and the chemical components present in the system.
-
-        Returns:
-            int: Number of aqueous species defined in the system. This includes
-                primary species (elements and basis species) and secondary species
-                (complexes) formed from the primary species.
-
-        Note:
-            The species count is determined after loading the database and
-            running initial equilibrium calculations. Species names can be
-            retrieved using RM_GetSpeciesName() with indices from 0 to count-1.
-
-        Examples:
-            >>> nspecies = rm.rm_get_species_count()
-            >>> print(f"System contains {nspecies} aqueous species")
-            >>> # Get all species names
-            >>> species_names = np.zeros(nspecies, dtype='U20')
-            >>> for i in range(nspecies):
-            >>>     rm.rm_get_species_name(i, species_names, 20)
-        """
-        return self.libc.RM_GetSpeciesCount(self.id)
-
-    def rm_get_species_d25(self, diffc) -> IRMStatus:
-        """Get diffusion coefficients at 25°C for all aqueous species.
-
-        Retrieves the reference diffusion coefficients (at 25°C in water)
-        for all aqueous species in the system. These values are used in
-        multicomponent diffusion calculations in reactive transport models.
-
-        Args:
-            diffc (numpy.ndarray): Array to receive diffusion coefficients
-                with length equal to the number of species. Units are m²/s.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Diffusion coefficients are taken from the thermodynamic database.
-            For transport at different temperatures, these values should be
-            corrected using appropriate temperature relationships.
-        """
-        return irm_result(self.libc.RM_GetSpeciesD25(self.id, diffc.ctypes))
-
-    def rm_get_species_log10_gammas(self, species_log10gammas) -> IRMStatus:
-        """Get log10 activity coefficients for all aqueous species.
-
-        Retrieves the base-10 logarithm of activity coefficients for all
-        aqueous species in each reaction cell. Activity coefficients account
-        for non-ideal solution behavior and are used to calculate activities
-        from concentrations.
-
-        Args:
-            species_log10gammas (numpy.ndarray): Array to receive log10 activity
-                coefficients with shape (nxyz * nspecies). Values are dimensionless.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Activity coefficients depend on ionic strength, temperature, and
-            the activity model used in the thermodynamic database (e.g.,
-            Debye-Hückel, Pitzer, SIT). Species saving must be enabled.
-        """
-        return irm_result(self.libc.RM_GetSpeciesLog10Gammas(self.id, species_log10gammas.ctypes))
-
-    def rm_get_species_name(self, num, chem_name, length) -> IRMStatus:
-        """Get the name of an aqueous species by index.
-
-        Retrieves the name of an aqueous species identified by its index.
-        Species names follow PHREEQC conventions and include charge states
-        for ionic species.
-
-        Args:
-            num (int): Index of the species (0-based).
-            chem_name (numpy.ndarray): String array to store species names.
-                The name will be stored at index num.
-            length (int): Maximum length of the species name string.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Examples:
-            >>> nspecies = rm.rm_get_species_count()
-            >>> species_names = np.zeros(nspecies, dtype='U20')
-            >>> for i in range(nspecies):
-            >>>     rm.rm_get_species_name(i, species_names, 20)
-            >>> print(species_names)  # ['H2O', 'H+', 'OH-', 'Ca+2', ...]
-        """
-        string = ctypes.create_string_buffer(length)
-        status = self.libc.RM_GetSpeciesName(self.id, num, string, length)
-        chem_name[num] = string.value.decode()
-        return irm_result(status)
-
-    def rm_get_species_save_on(self) -> int:
-        """Check if species concentration saving is enabled.
-
-        Returns the current setting for species concentration saving. When
-        enabled, PhreeqcRM calculates and stores individual species
-        concentrations that can be retrieved with RM_GetSpeciesConcentrations().
-
-        Returns:
-            int: 1 if species saving is enabled, 0 if disabled.
-
-        Note:
-            Species saving increases memory usage and computation time but
-            provides detailed speciation information useful for analysis
-            and multicomponent diffusion calculations.
-        """
-        return self.libc.RM_GetSpeciesSaveOn(self.id)
-
-    def rm_get_species_z(self, z) -> IRMStatus:
-        """Get charge values for all aqueous species.
-
-        Retrieves the electric charge (valence) for each aqueous species
-        in the system. Charge values are essential for electrostatic
-        calculations and charge balance constraints.
-
-        Args:
-            z (numpy.ndarray): Array to receive charge values with length
-                equal to the number of species. Values are dimensionless
-                (e.g., +2 for Ca+2, -1 for Cl-, 0 for neutral species).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Charge values are defined in the thermodynamic database and
-            are used in activity coefficient calculations and electroneutrality
-            constraints.
-        """
-        return irm_result(self.libc.RM_GetSpeciesZ(self.id, z.ctypes))
-
-    def rm_get_start_cell(self, sc):
-        return self.libc.RM_GetStartCell(self.id, sc)
-
-    def rm_get_surface_name(self, num, name, l1):
-        return self.libc.RM_GetSurfaceName(self.id, num, name, l1)
-
-    def rm_get_surface_type(self, num, name, l1):
-        return self.libc.RM_GetSurfaceType(self.id, num, name, l1)
-
-    def rm_get_thread_count(self):
-        return self.libc.RM_GetThreadCount(self.id)
-
-    def rm_get_time(self) -> float:
-        """Get the current simulation time.
-
-        Returns the current time in the reactive transport simulation.
-        This time is used for kinetic rate calculations and time-dependent
-        boundary conditions.
-
-        Returns:
-            float: Current simulation time in user-defined units (typically
-                seconds, days, or years depending on the model setup).
-
-        Note:
-            The simulation time is set by RM_SetTime() and is used internally
-            by PhreeqcRM for kinetic calculations. The time units should be
-            consistent with kinetic rate constants in the database.
-
-        Examples:
-            >>> current_time = rm.rm_get_time()
-            >>> print(f"Current simulation time: {current_time} days")
-        """
-        self.libc.RM_GetTime.restype = ctypes.c_double
-        return self.libc.RM_GetTime(self.id)
-
-    def rm_get_time_conversion(self) -> float:
-        self.libc.RM_GetTimeConversion.restype = ctypes.c_double
-        return self.libc.RM_GetTimeConversion(self.id)
-
-    def rm_get_time_step(self) -> float:
-        """Get the current time step duration.
-
-        Returns the duration of the current time step used for kinetic
-        calculations and time-dependent processes in the geochemical system.
-
-        Returns:
-            float: Current time step duration in user-defined units (typically
-                seconds, days, or years consistent with the simulation time units).
-
-        Note:
-            The time step is set by RM_SetTimeStep() and affects the integration
-            of kinetic rate equations. Smaller time steps provide more accurate
-            solutions but require more computational time.
-
-        Examples:
-            >>> dt = rm.rm_get_time_step()
-            >>> print(f"Current time step: {dt} days")
-        """
-        self.libc.RM_GetTimeStep.restype = ctypes.c_double
-        return self.libc.RM_GetTimeStep(self.id)
-
-    def rm_initial_phreeqc2_module(self, ic1, ic2, f1) -> IRMStatus:
-        """Transfer initial conditions from InitialPhreeqc to reaction module.
-
-        Args:
-            ic1 (numpy.ndarray): Initial condition indices for primary entities.
-            ic2 (numpy.ndarray): Initial condition indices for secondary entities.
-            f1 (numpy.ndarray): Mixing fractions for primary entities.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_InitialPhreeqc2Module(self.id, ic1.ctypes, ic2.ctypes, f1.ctypes))
-
-    def rm_initial_phreeqc2_concentrations(self, c, n_boundary, boundary_solution1, boundary_solution2, fraction1):
-        return self.libc.RM_InitialPhreeqc2Concentrations(
-            self.id, c.ctypes, n_boundary, boundary_solution1.ctypes, boundary_solution2.ctypes, fraction1.ctypes
-        )
-
-    def rm_initial_phreeqc2_species_concentrations(
-        self, species_c, n_boundary, boundary_solution1, boundary_solution2, fraction1
-    ):
-        return self.libc.RM_InitialPhreeqc2SpeciesConcentrations(
-            self.id,
-            species_c.ctypes,
-            n_boundary.ctypes,
-            boundary_solution1.ctypes,
-            boundary_solution2.ctypes,
-            fraction1.ctypes,
-        )
-
-    def rm_initial_phreeqc_cell2_module(self, n, module_numbers, dim_module_numbers):
-        return self.libc.RM_InitialPhreeqcCell2Module(self.id, n, module_numbers, dim_module_numbers)
-
-    def rm_load_database(self, db_name) -> IRMStatus:
-        """Load a thermodynamic database for geochemical calculations.
-
-        Loads a PHREEQC-format thermodynamic database containing thermodynamic
-        data for aqueous species, minerals, gases, and other phases. The database
-        defines the chemical system and enables geochemical calculations.
-
-        Args:
-            db_name (str): Path to the database file. Common databases include:
-                - "phreeqc.dat": Standard PHREEQC database (25°C)
-                - "Amm.dat": Extended database with ammonia species
-                - "pitzer.dat": Pitzer interaction parameter database
-                - "sit.dat": Specific ion interaction theory database
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Raises:
-            RuntimeError: If the database file cannot be found or loaded.
-
-        Note:
-            This method must be called before setting up initial conditions
-            or running calculations. The database determines which species,
-            minerals, and reactions are available for calculations.
-
-        Examples:
-            >>> rm = PhreeqcRM()
-            >>> rm.create(nxyz=100)
-            >>> result = rm.rm_load_database("phreeqc.dat")
-            >>> if not result:
-            >>>     raise RuntimeError(f"Failed to load database: {result}")
-        """
-        return irm_result(self.libc.RM_LoadDatabase(self.id, db_name.encode()))
-
-    def rm_log_message(self, str):
-        return self.libc.RM_LogMessage(self.id, str.encode())
-
-    def rm_mpi_worker(self):
-        return self.libc.RM_MpiWorker(self.id)
-
-    def rm_mpi_worker_break(self):
-        return self.libc.RM_MpiWorkerBreak(self.id)
-
-    def rm_open_files(self) -> IRMStatus:
-        """Open output files for logging, debugging, and error reporting.
-
-        Creates and opens output files for PhreeqcRM logging, error messages,
-        and debugging information. File names are based on the prefix set
-        by RM_SetFilePrefix().
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Files created:
-            - {prefix}.log: General log messages and information
-            - {prefix}.err: Error messages and warnings
-            - {prefix}.out: PHREEQC output from calculations
-
-        Note:
-            Call RM_SetFilePrefix() before this method to set the file name
-            prefix. Use RM_CloseFiles() to properly close files when finished.
-
-        Examples:
-            >>> rm.rm_set_file_prefix("simulation")
-            >>> rm.rm_open_files()  # Creates simulation.log, simulation.err, etc.
-            >>> # ... run calculations ...
-            >>> rm.rm_close_files()  # Clean up
-        """
-        return irm_result(self.libc.RM_OpenFiles(self.id))
-
-    def rm_output_message(self, str):
-        return self.libc.RM_OutputMessage(self.id, str.encode())
-
-    def rm_run_cells(self) -> IRMStatus:
-        """Run geochemical reactions for all reaction cells.
-
-        Performs equilibrium speciation and kinetic reactions for the current
-        time step in all reaction cells. This is the core computational method
-        that updates chemical compositions based on thermodynamic equilibrium
-        and reaction kinetics.
-
-        The method performs:
-            - Aqueous speciation calculations
-            - Mineral precipitation/dissolution equilibrium
-            - Ion exchange equilibrium
-            - Surface complexation equilibrium
-            - Gas phase equilibrium
-            - Kinetic reaction integration over the time step
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Before calling this method, ensure that:
-            - Concentrations are set with RM_SetConcentrations()
-            - Current time is set with RM_SetTime()
-            - Time step is set with RM_SetTimeStep()
-            - Temperature and pressure are set if needed
-
-        Examples:
-            >>> rm.rm_set_concentrations(concentrations)
-            >>> rm.rm_set_time(current_time)
-            >>> rm.rm_set_time_step(dt)
-            >>> result = rm.rm_run_cells()
-            >>> if result:
-            >>>     rm.rm_get_concentrations(updated_concentrations)
-            >>> else:
-            >>>     print(f"Reaction failed: {result}")
-        """
-        return irm_result(self.libc.RM_RunCells(self.id))
-
-    def rm_run_file(self, workers, initial_phreeqc, utility, chem_name) -> IRMStatus:
-        """Run a PHREEQC input file in specified PhreeqcRM instances.
-
-        Executes a PHREEQC input file (.pqi format) in one or more PhreeqcRM
-        instances. This is used to define initial conditions, equilibrium phases,
-        exchange assemblages, surface complexation sites, gas phases, solid
-        solutions, and kinetic reactions.
-
-        Args:
-            workers (int): Run in worker instances (1) or not (0). Worker instances
-                handle the main geochemical calculations for reaction cells.
-            initial_phreeqc (int): Run in initial PhreeqcRM instance (1) or not (0).
-                Used for defining initial chemical conditions and templates.
-            utility (int): Run in utility instance (1) or not (0). Utility instance
-                provides access to full PHREEQC functionality for special calculations.
-            chem_name (str): Path to the PHREEQC input file containing chemical
-                definitions and calculations.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            PHREEQC input files define the chemical system using standard PHREEQC
-            syntax. Common blocks include SOLUTION, EQUILIBRIUM_PHASES, EXCHANGE,
-            SURFACE, GAS_PHASE, SOLID_SOLUTIONS, KINETICS, and SELECTED_OUTPUT.
-
-        Examples:
-            >>> # Run initial conditions file in initial PhreeqcRM instance
-            >>> result = rm.rm_run_file(0, 1, 0, "initial_conditions.pqi")
-            >>> if not result:
-            >>>     print(f"Error running initial conditions file: {result}")
-        """
-        return irm_result(self.libc.RM_RunFile(self.id, workers, initial_phreeqc, utility, chem_name.encode()))
-
-    def rm_run_string(self, workers, initial_phreeqc, utility, input_string) -> IRMStatus:
-        """Run PHREEQC input from a string in specified instances.
-
-        Executes PHREEQC input commands provided as a string in one or more
-        PhreeqcRM instances. This allows dynamic generation of PHREEQC input
-        without creating temporary files.
-
-        Args:
-            workers (int): Run in worker instances (1) or not (0).
-            initial_phreeqc (int): Run in initial PhreeqcRM instance (1) or not (0).
-            utility (int): Run in utility instance (1) or not (0).
-            input_string (str): PHREEQC input commands as a string, using
-                standard PHREEQC syntax with newline separators.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Examples:
-            >>> phreeqc_input = '''
-            >>> SOLUTION 1
-            >>>     pH 7.0
-            >>>     Ca 1.0
-            >>>     Cl 2.0
-            >>> END
-            >>> '''
-            >>> rm.rm_run_string(0, 1, 0, phreeqc_input)
-        """
-        return irm_result(self.libc.RM_RunString(self.id, workers, initial_phreeqc, utility, input_string.encode()))
-
-    def rm_screen_message(self, str):
-        return self.libc.RM_ScreenMessage(self.id, str.encode())
-
-    def rm_set_component_h2o(self, tf) -> IRMStatus:
-        """Set whether to include H2O as a transport component.
-
-        Controls whether water (H2O) is included in the list of components
-        that must be transported in reactive transport simulations. This
-        setting affects the component count and transport requirements.
-
-        Args:
-            tf (int): Include H2O as a component:
-                1 = Include H2O as a transport component
-                0 = Exclude H2O from transport components (default)
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Typically, H2O is not transported as a separate component because
-            water content is determined by porosity, saturation, and density.
-            Including H2O increases the number of transport equations but may
-            be necessary for some specialized applications.
-
-        Examples:
-            >>> rm.rm_set_component_h2o(0)  # Standard: don't transport H2O
-            >>> ncomp = rm.rm_find_components()  # Get component count
-        """
-        return irm_result(self.libc.RM_SetComponentH2O(self.id, tf))
-
-    def rm_set_concentrations(self, c) -> IRMStatus:
-        """Set component concentrations for all reaction cells.
-
-        Transfers concentration data from the transport model to the reaction
-        module. This method is typically called at each transport time step
-        to provide updated concentrations for geochemical calculations.
-
-        Args:
-            c (numpy.ndarray): Concentration array with shape (nxyz * ncomps)
-                containing concentrations for all cells and components.
-                Concentrations must be in the units specified by RM_SetUnitsSolution().
-                Array is organized with all components for cell 0, followed by
-                all components for cell 1, etc.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            This method sets the starting concentrations for the next call to
-            RM_RunCells(). The concentrations are used as initial conditions
-            for equilibrium and kinetic calculations.
-
-        Examples:
-            >>> # Transport model updates concentrations
-            >>> new_conc = transport_step(old_conc, velocity, dt)
-            >>> # Transfer to reaction module
-            >>> result = rm.rm_set_concentrations(new_conc.flatten())
-            >>> if result:
-            >>>     rm.rm_run_cells()  # Run geochemical reactions
-        """
-        return irm_result(self.libc.RM_SetConcentrations(self.id, c.ctypes))
-
-    def rm_set_current_selected_output_user_number(self, n_user):
-        return self.libc.RM_SetCurrentSelectedOutputUserNumber(self.id, n_user)
-
-    def rm_set_density(self, density):
-        return self.libc.RM_SetDensity(self.id, density.ctypes)
-
-    def rm_set_dump_file_name(self, dump_name):
-        return self.libc.RM_SetDumpFileName(self.id, dump_name)
-
-    def rm_set_error_handler_mode(self, mode):
-        return self.libc.RM_SetErrorHandlerMode(self.id, mode)
-
-    def rm_set_file_prefix(self, prefix) -> IRMStatus:
-        """Set prefix for output files.
-
-        Args:
-            prefix (str): Prefix string for output file names.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_SetFilePrefix(self.id, prefix.encode()))
-
-    def rm_set_mpi_worker_callback_cookie(self, cookie):
-        return self.libc.RM_SetMpiWorkerCallbackCookie(self.id, cookie)
-
-    def rm_set_partition_uz_solids(self, tf):
-        return self.libc.RM_SetPartitionUZSolids(self.id, tf)
-
-    def rm_set_porosity(self, por) -> IRMStatus:
-        """Set porosity values for all grid cells.
-
-        Defines the porosity (void fraction) for each grid cell, which
-        represents the fraction of bulk volume occupied by pore space.
-        Porosity affects volume calculations for concentration conversions
-        and reaction extent calculations.
-
-        Args:
-            por (numpy.ndarray): Array of porosity values for each cell.
-                Length should equal the number of grid cells (nxyz).
-                Values must be between 0 and 1, where:
-                - 0 = no pore space (solid rock)
-                - 1 = completely porous (pure fluid)
-                - Typical values: 0.1-0.4 for sedimentary rocks
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Porosity is used with saturation to calculate the water volume
-            in each cell: water_volume = porosity × saturation × bulk_volume.
-            This affects concentration calculations and reaction stoichiometry.
-
-        Examples:
-            >>> porosity = np.full(nxyz, 0.25)  # 25% porosity for all cells
-            >>> rm.rm_set_porosity(porosity)
-        """
-        return irm_result(self.libc.RM_SetPorosity(self.id, por.ctypes))
-
-    def rm_set_pressure(self, p):
-        return self.libc.RM_SetPressure(self.id, p.ctypes)
-
-    def rm_set_print_chemistry_mask(self, cell_mask):
-        return self.libc.RM_SetPrintChemistryMask(self.id, cell_mask.ctypes)
-
-    def rm_set_print_chemistry_on(self, workers, initial_phreeqc, utility):
-        return self.libc.RM_SetPrintChemistryOn(self.id, workers, initial_phreeqc, utility)
-
-    def rm_set_rebalance_by_cell(self, method):
-        return self.libc.RM_SetRebalanceByCell(self.id, method)
-
-    def rm_set_rebalance_fraction(self, f) -> IRMStatus:
-        """Set load balancing algorithm fraction.
-
-        Args:
-            f (float): Fraction for load balancing (typically 0.5).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_SetRebalanceFraction(self.id, ctypes.c_double(f)))
-
-    def rm_set_representative_volume(self, rv):
-        return self.libc.RM_SetRepresentativeVolume(self.id, rv.ctypes)
-
-    def rm_set_saturation(self, sat) -> IRMStatus:
-        """Set water saturation values for all grid cells.
-
-        Defines the water saturation for each grid cell, representing the
-        fraction of pore space occupied by water. Saturation affects volume
-        calculations and is particularly important in unsaturated zone
-        modeling where gas phase may occupy part of the pore space.
-
-        Args:
-            sat (numpy.ndarray): Array of saturation values for each cell.
-                Length should equal the number of grid cells (nxyz).
-                Values must be between 0 and 1, where:
-                - 0 = dry (no water in pores)
-                - 1 = fully saturated (all pores filled with water)
-                - Typical values: 0.1-1.0 depending on vadose zone conditions
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            Water saturation is used with porosity to calculate the actual
-            water volume: water_volume = porosity × saturation × bulk_volume.
-            This directly affects solution concentrations and reaction rates.
-
-        Examples:
-            >>> # Fully saturated conditions
-            >>> saturation = np.ones(nxyz)
-            >>> rm.rm_set_saturation(saturation)
-            >>>
-            >>> # Partially saturated (vadose zone)
-            >>> sat_profile = np.linspace(0.3, 1.0, nxyz)  # Increasing with depth
-            >>> rm.rm_set_saturation(sat_profile)
-        """
-        return irm_result(self.libc.RM_SetSaturation(self.id, sat.ctypes))
-
-    def rm_set_screen_on(self, tf):
-        return self.libc.RM_SetScreenOn(self.id, tf)
-
-    def rm_set_selected_output_on(self, selected_output):
-        return self.libc.RM_SetSelectedOutputOn(self.id, selected_output)
-
-    def rm_set_species_save_on(self, save_on) -> IRMStatus:
-        """Enable or disable saving of aqueous species concentrations.
-
-        Controls whether PhreeqcRM calculates and stores individual aqueous
-        species concentrations that can be retrieved with RM_GetSpeciesConcentrations().
-        This provides detailed speciation information but increases memory usage
-        and computation time.
-
-        Args:
-            save_on (int): Species saving option:
-                1 = Enable species concentration saving
-                0 = Disable species saving (default, saves memory and time)
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            When enabled, PhreeqcRM stores concentrations for all aqueous species
-            after each call to RM_RunCells(). This is required for:
-            - Multicomponent diffusion calculations
-            - Detailed speciation analysis
-            - Species-specific output and post-processing
-
-        Examples:
-            >>> rm.rm_set_species_save_on(1)  # Enable species saving
-            >>> rm.rm_run_cells()  # Calculate with species saving
-            >>> species_conc = np.zeros(nxyz * nspecies)
-            >>> rm.rm_get_species_concentrations(species_conc)
-        """
-        return irm_result(self.libc.RM_SetSpeciesSaveOn(self.id, save_on))
-
-    def rm_set_temperature(self, t):
-        return self.libc.RM_SetTemperature(self.id, t.ctypes)
-
-    def rm_set_time(self, time) -> IRMStatus:
-        """Set the current simulation time for geochemical calculations.
-
-        Updates the simulation time used by PhreeqcRM for kinetic rate
-        calculations and time-dependent processes. The time should be
-        consistent with the time stepping in the transport model.
-
-        Args:
-            time (float): Current simulation time in user-defined units.
-                Units should be consistent with kinetic rate constants
-                in the thermodynamic database (typically seconds, days, or years).
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            This method should be called before each call to RM_RunCells()
-            to ensure kinetic calculations use the correct time. The time
-            is used to integrate kinetic rate equations over the time step.
-
-        Examples:
-            >>> for step in range(num_steps):
-            >>>     current_time = step * dt
-            >>>     rm.rm_set_time(current_time)
-            >>>     rm.rm_set_time_step(dt)
-            >>>     rm.rm_run_cells()
-        """
-        return irm_result(self.libc.RM_SetTime(self.id, ctypes.c_double(time)))
-
-    def rm_set_time_conversion(self, conv_factor):
-        return self.libc.RM_SetTimeConversion(self.id, ctypes.c_double(conv_factor))
-
-    def rm_set_time_step(self, time_step) -> IRMStatus:
-        """Set the time step duration for kinetic calculations.
-
-        Specifies the time interval over which kinetic reactions will be
-        integrated during the next call to RM_RunCells(). The time step
-        affects the accuracy of kinetic calculations.
-
-        Args:
-            time_step (float): Time step duration in user-defined units.
-                Must be positive and consistent with simulation time units.
-                Smaller time steps provide better accuracy for fast kinetic
-                reactions but increase computational cost.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            The time step is used for integrating kinetic rate equations.
-            For stiff kinetic systems, smaller time steps may be required
-            for numerical stability and accuracy.
-
-        Examples:
-            >>> dt = 0.1  # 0.1 day time step
-            >>> rm.rm_set_time_step(dt)
-            >>> rm.rm_run_cells()  # Integrate kinetics over dt
-        """
-        return irm_result(self.libc.RM_SetTimeStep(self.id, ctypes.c_double(time_step)))
-
-    def rm_set_units_exchange(self, option) -> IRMStatus:
-        """Set units for exchange reactions.
-
-        Args:
-            option (int): Units option for exchange calculations.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_SetUnitsExchange(self.id, option))
-
-    def rm_set_units_gas_phase(self, option) -> None:
-        self.libc.RM_SetUnitsGasPhase(self.id, option)
-
-    def rm_set_units_kinetics(self, option) -> None:
-        self.libc.RM_SetUnitsKinetics(self.id, option)
-
-    def rm_set_units_p_passemblage(self, option):
-        return self.libc.RM_SetUnitsPPassemblage(self.id, option)
-
-    def rm_set_units_solution(self, option) -> IRMStatus:
-        """Set concentration units for aqueous solutions.
-
-        Specifies the units for solution concentrations used in all
-        concentration transfers between the transport model and PhreeqcRM.
-        This affects how concentration data is interpreted and returned.
-
-        Args:
-            option (int): Units option for solution concentrations:
-                1 = mol/L (molar)
-                2 = mmol/L (millimolar) - commonly used
-                3 = μmol/L (micromolar) - for trace species
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-
-        Note:
-            This setting affects:
-            - RM_SetConcentrations() input interpretation
-            - RM_GetConcentrations() output units
-            - RM_GetSpeciesConcentrations() output units
-            - Initial condition concentration scaling
-
-        Examples:
-            >>> rm.rm_set_units_solution(2)  # Use mmol/L
-            >>> # Now all concentrations are in millimolar units
-            >>> conc_mmol = np.array([1.0, 0.5, 2.0])  # 1, 0.5, 2 mmol/L
-            >>> rm.rm_set_concentrations(conc_mmol)
-        """
-        return irm_result(self.libc.RM_SetUnitsSolution(self.id, option))
-
-    def rm_set_units_ss_assemblage(self, option):
-        return self.libc.RM_SetUnitsSSassemblage(self.id, option)
-
-    def rm_set_units_surface(self, option) -> IRMStatus:
-        """Set units for surface complexation reactions.
-
-        Args:
-            option (int): Units option for surface calculations.
-
-        Returns:
-            IRMStatus: Status object with code, name, and message. Use bool(result) to check success.
-        """
-        return irm_result(self.libc.RM_SetUnitsSurface(self.id, option))
-
-    def rm_species_concentrations2_module(self, species_conc):
-        return self.libc.RM_SpeciesConcentrations2Module(self.id, species_conc.ctypes)
-
-    def rm_use_solution_density_volume(self, tf):
-        return self.libc.RM_UseSolutionDensityVolume(self.id, tf)
-
-    def rm_warning_message(self, warn_str):
-        return self.libc.RM_WarningMessage(self.id, warn_str)
-
-    def rm_get_component_count(self) -> int:
-        """Get the number of transport components in the system.
-
-        Returns the number of chemical components (elements plus charge balance)
-        that must be transported in reactive transport simulations. This count
-        is determined after calling RM_FindComponents() and defines the size
-        of concentration arrays.
-
-        Returns:
-            int: Number of transport components, which includes:
-                - Chemical elements present in the system (e.g., Ca, Cl, C)
-                - Electric charge balance component
-                - Isotopes if defined in the database
-                - Excludes H2O unless RM_SetComponentH2O(1) was called
-
-        Note:
-            This method should be called after RM_FindComponents() to get the
-            correct component count. The returned value determines array sizes
-            for RM_SetConcentrations() and RM_GetConcentrations().
-
-        Examples:
-            >>> rm.run_initial_from_file("initial.pqi", ic_array)
-            >>> ncomp = rm.rm_get_component_count()
-            >>> conc_array = np.zeros(nxyz * ncomp)
-            >>> rm.rm_get_concentrations(conc_array)
-        """
-        return self.libc.RM_GetComponentCount(self.id)
+        headings = list(self.rm.GetSelectedOutputHeadings())
+        ncolsel = len(headings)
+        so = np.array(self.rm.GetSelectedOutput())
+        return pd.DataFrame(so.reshape(ncolsel, self.nxyz).T, columns=headings)
diff --git a/pyproject.toml b/pyproject.toml
index dba49b3..b38520f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
     "matplotlib",
     "pyqt5",
     "scipy",
+    "phreeqcrm",
 ]
 description = "Microbiome Bioremediation Reaction Module: a Python interface to PhreeqcRM library"
 keywords = ["Microbiome"," Bioremediation"," Chemical Reaction"," Geochemical Engineering"]
@@ -73,6 +74,9 @@ packages = ["mibiremo"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
+filterwarnings = [
+    "ignore:builtin type SwigPy:DeprecationWarning",
+]
 
 [tool.coverage.run]
 branch = true
diff --git a/tests/test_irmresult.py b/tests/test_irmresult.py
deleted file mode 100644
index 1523ae8..0000000
--- a/tests/test_irmresult.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""Tests for the mibiremo.irmresult module."""
-
-import pytest
-from mibiremo.irmresult import IRMStatus
-
-
-class TestIRMStatus:
-    """Test the IRMStatus named tuple and its methods."""
-
-    def test_irmstatus_creation(self):
-        """Test IRMStatus creation with all attributes."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-        assert status.code == 0
-        assert status.name == "IRM_OK"
-        assert status.message == "Success"
-
-    def test_bool_success(self):
-        """Test __bool__ method for success case."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-        assert bool(status) is True
-        assert status  # Direct boolean evaluation
-
-    def test_bool_failure(self):
-        """Test __bool__ method for failure cases."""
-        status = IRMStatus(-1, "IRM_OUTOFMEMORY", "Failure, Out of memory")
-        assert bool(status) is False
-        assert not status  # Direct boolean evaluation
-
-        # Test with various failure codes
-        for code in [-1, -2, -3, -4, -5, -6, -7, 1, 42]:
-            status = IRMStatus(code, "ERROR", "Some error")
-            assert bool(status) is False
-
-    def test_int_conversion(self):
-        """Test __int__ method for backwards compatibility."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-        assert int(status) == 0
-
-        status = IRMStatus(-5, "IRM_INVALIDCOL", "Failure, Invalid column")
-        assert int(status) == -5
-
-        status = IRMStatus(42, "CUSTOM", "Custom error")
-        assert int(status) == 42
-
-    def test_str_representation(self):
-        """Test __str__ method for formatted output."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-        assert str(status) == "IRM_OK: Success"
-
-        status = IRMStatus(-1, "IRM_OUTOFMEMORY", "Failure, Out of memory")
-        assert str(status) == "IRM_OUTOFMEMORY: Failure, Out of memory"
-
-        status = IRMStatus(99, "UNKNOWN", "Unknown error")
-        assert str(status) == "UNKNOWN: Unknown error"
-
-    def test_raise_for_status_success(self):
-        """Test raise_for_status method with success status."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-        # Should not raise any exception
-        status.raise_for_status()
-        status.raise_for_status("Operation context")
-
-    def test_raise_for_status_failure(self):
-        """Test raise_for_status method with failure status."""
-        status = IRMStatus(-1, "IRM_OUTOFMEMORY", "Failure, Out of memory")
-
-        # Test without context
-        with pytest.raises(RuntimeError, match="IRM_OUTOFMEMORY: Failure, Out of memory"):
-            status.raise_for_status()
-
-    def test_raise_for_status_failure_with_context(self):
-        """Test raise_for_status method with failure status and context."""
-        status = IRMStatus(-3, "IRM_INVALIDARG", "Failure, Invalid argument")
-
-        # Test with context
-        with pytest.raises(RuntimeError, match="Loading database: IRM_INVALIDARG: Failure, Invalid argument"):
-            status.raise_for_status("Loading database")
-
-        # Test with empty context string
-        with pytest.raises(RuntimeError, match="IRM_INVALIDARG: Failure, Invalid argument"):
-            status.raise_for_status("")
-
-    def test_named_tuple_behavior(self):
-        """Test that IRMStatus behaves like a named tuple."""
-        status = IRMStatus(0, "IRM_OK", "Success")
-
-        # Test tuple unpacking
-        code, name, message = status
-        assert code == 0
-        assert name == "IRM_OK"
-        assert message == "Success"
-
-        # Test indexing
-        assert status[0] == 0
-        assert status[1] == "IRM_OK"
-        assert status[2] == "Success"
-
-        # Test immutability
-        with pytest.raises(AttributeError):
-            status.code = 1
diff --git a/tests/test_phreeqcrm.py b/tests/test_phreeqcrm.py
index ca708cb..193ab70 100644
--- a/tests/test_phreeqcrm.py
+++ b/tests/test_phreeqcrm.py
@@ -1,5 +1,5 @@
 """Tests for the mibiremo.phreeqcrm module.
-The C library calls are mocked to allow testing without the actual library.
+The phreeqcrm.PhreeqcRM class is mocked to allow testing without the actual library.
 The objective is to test the Python wrapper logic.
 """
 
@@ -18,20 +18,17 @@ def test_phreeqcrm_init(self):
         phr = mibiremo.PhreeqcRM()
         assert phr is not None
         assert not phr._initialized
-        assert phr.dllpath is None
         assert phr.nxyz == 1
         assert phr.n_threads == 1
-        assert phr.libc is None
-        assert phr.id is None
+        assert phr.rm is None
         assert phr.components is None
         assert phr.species is None
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_default_params(self, mock_cdll):
-        """Test create method with default parameters, mocking the C library."""
-        mock_lib = MagicMock()  # Create a mock to simulate the C library
-        mock_lib.RM_Create.return_value = 1  # Simulate successful instance creation
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_create_default_params(self, mock_phreeqcrm_cls):
+        """Test create method with default parameters."""
+        mock_rm = MagicMock()
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create()
@@ -39,15 +36,14 @@ def test_create_default_params(self, mock_cdll):
         assert phr._initialized
         assert phr.nxyz == 1
         assert phr.n_threads == 1
-        assert phr.id == 1
-        mock_lib.RM_Create.assert_called_once_with(1, 1)
+        assert phr.rm is mock_rm
+        mock_phreeqcrm_cls.assert_called_once_with(1, 1)
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_custom_params(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_create_custom_params(self, mock_phreeqcrm_cls):
         """Test create method with custom parameters."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 2
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=100, n_threads=4)
@@ -55,65 +51,27 @@ def test_create_custom_params(self, mock_cdll):
         assert phr._initialized
         assert phr.nxyz == 100
         assert phr.n_threads == 4
-        assert phr.id == 2
-        mock_lib.RM_Create.assert_called_once_with(100, 4)
+        assert phr.rm is mock_rm
+        mock_phreeqcrm_cls.assert_called_once_with(100, 4)
 
     @patch("mibiremo.phreeqc.os.cpu_count")
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_auto_threads(self, mock_cdll, mock_cpu_count):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_create_auto_threads(self, mock_phreeqcrm_cls, mock_cpu_count):
         """Test automatic detection of CPU threads."""
         mock_cpu_count.return_value = 8
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 3
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=50, n_threads=-1)
 
         assert phr.n_threads == 8
-        mock_lib.RM_Create.assert_called_once_with(50, 8)
+        mock_phreeqcrm_cls.assert_called_once_with(50, 8)
 
-    @patch("mibiremo.phreeqc.os.name", "nt")
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_windows_dll_path(self, mock_cdll):
-        """Test DLL path generation on Windows."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 4
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        assert phr.dllpath.endswith("PhreeqcRM.dll")
-        assert "lib" in phr.dllpath
-
-    @patch("mibiremo.phreeqc.os.name", "posix")
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_linux_dll_path(self, mock_cdll):
-        """Test DLL path generation on Linux."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 5
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        assert phr.dllpath.endswith("PhreeqcRM.so")
-        assert "lib" in phr.dllpath
-
-    @patch("mibiremo.phreeqc.os.name", "unknown")
-    def test_create_unsupported_os(self):
-        """Test exception for unsupported operating system."""
-        phr = mibiremo.PhreeqcRM()
-        with pytest.raises(Exception, match="Operating system not supported"):
-            phr.create()
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_create_failure(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_create_failure(self, mock_phreeqcrm_cls):
         """Test failure in PhreeqcRM instance creation."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.side_effect = Exception("Creation failed")
-        mock_cdll.return_value = mock_lib
+        mock_phreeqcrm_cls.side_effect = Exception("Creation failed")
 
         phr = mibiremo.PhreeqcRM()
         with pytest.raises(RuntimeError, match="Failed to create PhreeqcRM instance"):
@@ -123,40 +81,37 @@ def test_create_failure(self, mock_cdll):
 class TestPhreeqcRMInitializePhreeqc:
     """Test PhreeqcRM initialization with database and parameters."""
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_initialize_phreeqc_not_initialized(self, mock_cdll):
+    def test_initialize_phreeqc_not_initialized(self):
         """Test initialize_phreeqc before create() is called."""
         phr = mibiremo.PhreeqcRM()
         with pytest.raises(RuntimeError, match="PhreeqcRM instance not initialized"):
             phr.initialize_phreeqc("test.dat")
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_initialize_phreeqc_success(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_initialize_phreeqc_success(self, mock_phreeqcrm_cls):
         """Test successful initialize_phreeqc call."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_LoadDatabase.return_value = 0  # Success
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.LoadDatabase.return_value = 0
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=10)
         phr.initialize_phreeqc("phreeqc.dat")
 
-        # Verify key method calls
-        mock_lib.RM_LoadDatabase.assert_called_once_with(1, b"phreeqc.dat")
-        mock_lib.RM_SetComponentH2O.assert_called_once_with(1, 0)
-        mock_lib.RM_SetRebalanceFraction.assert_called_once()
-        mock_lib.RM_SetUnitsSolution.assert_called_once_with(1, 2)
-        mock_lib.RM_SetFilePrefix.assert_called_once_with(1, b"phr")
-        mock_lib.RM_OpenFiles.assert_called_once_with(1)
+        mock_rm.LoadDatabase.assert_called_once_with("phreeqc.dat")
+        mock_rm.SetComponentH2O.assert_called_once_with(False)
+        mock_rm.SetRebalanceFraction.assert_called_once_with(0.5)
+        mock_rm.SetUnitsSolution.assert_called_once_with(2)
+        mock_rm.SetFilePrefix.assert_called_once_with("phr")
+        mock_rm.OpenFiles.assert_called_once()
+        mock_rm.SetSpeciesSaveOn.assert_called_once_with(True)
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_initialize_phreeqc_database_failure(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_initialize_phreeqc_database_failure(self, mock_phreeqcrm_cls):
         """Test initialize_phreeqc with database loading failure."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_LoadDatabase.return_value = -1  # Failure
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.LoadDatabase.return_value = -1
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=10)
@@ -164,13 +119,12 @@ def test_initialize_phreeqc_database_failure(self, mock_cdll):
         with pytest.raises(RuntimeError, match="Failed to load Phreeqc database"):
             phr.initialize_phreeqc("invalid.dat")
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_initialize_phreeqc_custom_params(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_initialize_phreeqc_custom_params(self, mock_phreeqcrm_cls):
         """Test initialize_phreeqc with custom parameters."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_LoadDatabase.return_value = 0
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.LoadDatabase.return_value = 0
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=5)
@@ -178,46 +132,43 @@ def test_initialize_phreeqc_custom_params(self, mock_cdll):
             "custom.dat", units_solution=1, units=2, porosity=0.3, saturation=0.8, multicomponent=False
         )
 
-        mock_lib.RM_SetUnitsSolution.assert_called_with(1, 1)
-        mock_lib.RM_SetUnitsPPassemblage.assert_called_with(1, 2)
-        mock_lib.RM_SetSpeciesSaveOn.assert_not_called()  # multicomponent=False
+        mock_rm.SetUnitsSolution.assert_called_with(1)
+        mock_rm.SetUnitsPPassemblage.assert_called_with(2)
+        mock_rm.SetSpeciesSaveOn.assert_not_called()
 
 
 class TestPhreeqcRMRunInitialFromFile:
     """Test running initial conditions from file."""
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_initial_from_file_success(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_success(self, mock_phreeqcrm_cls):
         """Test successful run_initial_from_file."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = 0  # Success
-        mock_lib.RM_InitialPhreeqc2Module.return_value = 0
-        mock_lib.RM_FindComponents.return_value = 3
-        mock_lib.RM_GetSpeciesCount.return_value = 10
-        mock_lib.RM_SetTime.return_value = 0
-        mock_lib.RM_SetTimeStep.return_value = 0
-        mock_lib.RM_RunCells.return_value = 0
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_rm.GetComponents.return_value = ["H", "O", "Charge"]
+        mock_rm.GetSpeciesNames.return_value = ["H+", "OH-", "H2O"]
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=2)
 
         ic = np.array([[1, -1, -1, -1, -1, -1, -1], [2, -1, -1, -1, -1, -1, -1]])
-
         phr.run_initial_from_file("test.pqi", ic)
 
-        mock_lib.RM_RunFile.assert_called_once_with(1, 1, 1, 1, b"test.pqi")
+        mock_rm.RunFile.assert_called_once_with(True, True, True, "test.pqi")
+        mock_rm.InitialPhreeqc2Module.assert_called_once()
+        mock_rm.FindComponents.assert_called_once()
         assert phr.components is not None
         assert phr.species is not None
+        np.testing.assert_array_equal(phr.components, ["H", "O", "Charge"])
+        np.testing.assert_array_equal(phr.species, ["H+", "OH-", "H2O"])
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_initial_from_file_failure(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_failure(self, mock_phreeqcrm_cls):
         """Test run_initial_from_file with file failure."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = -1  # Failure
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = -1
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=1)
@@ -227,574 +178,144 @@ def test_run_initial_from_file_failure(self, mock_cdll):
         with pytest.raises(RuntimeError, match="Failed to run Phreeqc input file"):
             phr.run_initial_from_file("invalid.pqi", ic)
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_initial_from_file_wrong_ic_shape(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_wrong_ic_shape(self, mock_phreeqcrm_cls):
         """Test run_initial_from_file with incorrect IC array shape."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = 0  # Success for file run
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=1)  # Need to create first
+        phr.create(nxyz=1)
 
-        # Wrong shape - should be (nxyz, 7)
         ic = np.array([[1, -1, -1, -1]])
 
         with pytest.raises(ValueError, match="Initial conditions array must have shape"):
             phr.run_initial_from_file("test.pqi", ic)
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_initial_from_file_invalid_ic_type(self, mock_cdll):
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_invalid_ic_type(self, mock_phreeqcrm_cls):
         """Test run_initial_from_file with non-convertible IC data."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = 0  # Success for file run
-        mock_cdll.return_value = mock_lib
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=1)
 
-        # Create a numpy array with string data that can't be converted to int
         ic = np.array([["invalid", "data", "here", "x", "y", "z", "w"]])
 
         with pytest.raises(ValueError, match="invalid literal for int"):
             phr.run_initial_from_file("test.pqi", ic)
 
-
-class TestPhreeqcRMSelectedOutput:
-    """Test selected output functionality."""
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_get_selected_output_df_basic(self, mock_cdll):
-        """Test get_selected_output_df method basic functionality."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_GetSelectedOutputColumnCount.return_value = 2
-        mock_lib.RM_GetSelectedOutputHeading.return_value = 0
-        mock_lib.RM_GetSelectedOutput.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        # Mock the string buffer creation for headings
-        def mock_get_heading(id, col, buffer, length):
-            buffer.value = f"col_{col}".encode()
-            return 0
-
-        mock_lib.RM_GetSelectedOutputHeading.side_effect = mock_get_heading
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_non_ndarray_success(self, mock_phreeqcrm_cls):
+        """Test run_initial_from_file with non-ndarray IC that converts to int."""
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_rm.GetComponents.return_value = ["H"]
+        mock_rm.GetSpeciesNames.return_value = ["H+"]
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=2)
-
-        with patch("mibiremo.phreeqc.ctypes.create_string_buffer") as mock_buffer:
-            mock_str_buffer = MagicMock()
-            mock_str_buffer.value.decode.return_value = "test_heading"
-            mock_buffer.return_value = mock_str_buffer
-
-            df = phr.get_selected_output_df()
-
-            assert isinstance(df, pd.DataFrame)
-            # Verify the method was called
-            mock_lib.RM_GetSelectedOutputColumnCount.assert_called_once()
-            mock_lib.RM_GetSelectedOutput.assert_called_once()
-
-    def test_selected_output_column_count(self):
-        """Test RM_GetSelectedOutputColumnCount method directly."""
-        with patch("mibiremo.phreeqc.ctypes.CDLL") as mock_cdll:
-            mock_lib = MagicMock()
-            mock_lib.RM_Create.return_value = 1
-            mock_lib.RM_GetSelectedOutputColumnCount.return_value = 5
-            mock_cdll.return_value = mock_lib
-
-            phr = mibiremo.PhreeqcRM()
-            phr.create()
-
-            result = phr.rm_get_selected_output_column_count()
-            assert result == 5
-
-
-class TestPhreeqcRMGetterMethods:
-    """Test various getter methods."""
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_basic_getters(self, mock_cdll):
-        """Test basic getter methods that return simple values."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_GetGridCellCount.return_value = 100
-        mock_lib.RM_GetChemistryCellCount.return_value = 100
-        mock_lib.RM_GetComponentCount.return_value = 5
-        mock_lib.RM_GetSpeciesCount.return_value = 20
-        mock_lib.RM_GetEquilibriumPhaseCount.return_value = 3
-        mock_lib.RM_GetThreadCount.return_value = 4
-        mock_lib.RM_GetSpeciesSaveOn.return_value = 1
-        mock_lib.RM_GetSelectedOutputColumnCount.return_value = 10
-        mock_lib.RM_GetSelectedOutputRowCount.return_value = 100
-        mock_lib.RM_GetTime.return_value = 1000.0
-        mock_lib.RM_GetTimeStep.return_value = 1.0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=100)
-
-        assert phr.rm_get_grid_cell_count() == 100
-        assert phr.rm_get_chemistry_cell_count() == 100
-        assert phr.rm_get_component_count() == 5
-        assert phr.rm_get_species_count() == 20
-        assert phr.rm_get_equilibrium_phase_count() == 3
-        assert phr.rm_get_thread_count() == 4
-        assert phr.rm_get_species_save_on() == 1
-        assert phr.rm_get_selected_output_column_count() == 10
-        assert phr.rm_get_selected_output_row_count() == 100
-        assert phr.rm_get_time() == 1000.0
-        assert phr.rm_get_time_step() == 1.0
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_array_getters(self, mock_cdll):
-        """Test getter methods that fill arrays."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_GetConcentrations.return_value = 0
-        mock_lib.RM_GetSpeciesConcentrations.return_value = 0
-        mock_lib.RM_GetDensity.return_value = 0
-        mock_lib.RM_GetSaturation.return_value = 0
-        mock_lib.RM_GetGfw.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=10)
-
-        # Test concentration getter
-        conc = np.zeros(50)  # 10 cells * 5 components
-        result = phr.rm_get_concentrations(conc)
-        assert result.code == 0
-
-        # Test species concentration getter
-        species_conc = np.zeros(200)  # 10 cells * 20 species
-        result = phr.rm_get_species_concentrations(species_conc)
-        assert result.code == 0
-
-        # Test density getter
-        density = np.zeros(10)
-        result = phr.rm_get_density(density)
-        assert result.code == 0
-
-        # Test saturation getter
-        saturation = np.zeros(10)
-        result = phr.rm_get_saturation(saturation)
-        assert result.code == 0
-
-        # Test gram formula weights
-        gfw = np.zeros(5)
-        result = phr.rm_get_gfw(gfw)
-        assert result.code == 0
-
-
-class TestPhreeqcRMSetterMethods:
-    """Test various setter methods."""
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_basic_setters(self, mock_cdll):
-        """Test basic setter methods."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_SetComponentH2O.return_value = 0
-        mock_lib.RM_SetSpeciesSaveOn.return_value = 0
-        mock_lib.RM_SetUnitsSolution.return_value = 0
-        mock_lib.RM_SetUnitsExchange.return_value = 0
-        mock_lib.RM_SetUnitsSurface.return_value = 0
-        mock_lib.RM_SetTime.return_value = 0
-        mock_lib.RM_SetTimeStep.return_value = 0
-        mock_lib.RM_SetRebalanceFraction.return_value = 0
-        mock_lib.RM_SetFilePrefix.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        # Test various setter methods
-        result = phr.rm_set_component_h2o(1)
-        assert result.code == 0
-
-        result = phr.rm_set_species_save_on(1)
-        assert result.code == 0
-
-        result = phr.rm_set_units_solution(2)
-        assert result.code == 0
-
-        result = phr.rm_set_units_exchange(1)
-        assert result.code == 0
-
-        result = phr.rm_set_units_surface(1)
-        assert result.code == 0
-
-        result = phr.rm_set_time(100.0)
-        assert result.code == 0
-
-        result = phr.rm_set_time_step(1.0)
-        assert result.code == 0
-
-        result = phr.rm_set_rebalance_fraction(0.5)
-        assert result.code == 0
-
-        result = phr.rm_set_file_prefix("test")
-        assert result.code == 0
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_array_setters(self, mock_cdll):
-        """Test setter methods that take arrays."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_SetConcentrations.return_value = 0
-        mock_lib.RM_SetPorosity.return_value = 0
-        mock_lib.RM_SetSaturation.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=5)
-
-        # Test concentration setter
-        conc = np.ones(25)  # 5 cells * 5 components
-        result = phr.rm_set_concentrations(conc)
-        assert result.code == 0
-
-        # Test porosity setter
-        porosity = np.full(5, 0.3)
-        result = phr.rm_set_porosity(porosity)
-        assert result.code == 0
-
-        # Test saturation setter
-        saturation = np.full(5, 0.8)
-        result = phr.rm_set_saturation(saturation)
-        assert result.code == 0
-
-
-class TestPhreeqcRMRunMethods:
-    """Test methods that run PHREEQC calculations."""
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_cells(self, mock_cdll):
-        """Test RM_RunCells method."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunCells.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        result = phr.rm_run_cells()
-        assert result.code == 0
-        assert result
-        mock_lib.RM_RunCells.assert_called_once_with(1)
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_file(self, mock_cdll):
-        """Test RM_RunFile method."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        result = phr.rm_run_file(1, 1, 0, "test.pqi")
-        assert result.code == 0
-        mock_lib.RM_RunFile.assert_called_once_with(1, 1, 1, 0, b"test.pqi")
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_string(self, mock_cdll):
-        """Test RM_RunString method."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunString.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        input_string = "SOLUTION 1\npH 7\nEND"
-        result = phr.rm_run_string(0, 1, 0, input_string)
-        assert result.code == 0
-        mock_lib.RM_RunString.assert_called_once_with(1, 0, 1, 0, input_string.encode())
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_load_database(self, mock_cdll):
-        """Test RM_LoadDatabase method."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_LoadDatabase.return_value = 0
-        mock_cdll.return_value = mock_lib
+        phr.create(nxyz=1)
 
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
+        ic = pd.DataFrame([[1, -1, -1, -1, -1, -1, -1]])
+        phr.run_initial_from_file("test.pqi", ic)
 
-        result = phr.rm_load_database("phreeqc.dat")
-        assert result.code == 0
-        mock_lib.RM_LoadDatabase.assert_called_once_with(1, b"phreeqc.dat")
+        mock_rm.InitialPhreeqc2Module.assert_called_once()
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_run_initial_ic_conversion_error(self, mock_cdll):
-        """Test run_initial_from_file with IC conversion that fails."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_RunFile.return_value = 0
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_non_ndarray_failure(self, mock_phreeqcrm_cls):
+        """Test run_initial_from_file with non-ndarray IC that cannot convert."""
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
         phr.create(nxyz=1)
 
-        # Create a numpy array with non-convertible data
-        ic_array = np.array([["text", "data", "here", "x", "y", "z", "w"]], dtype=str)
-
-        with pytest.raises(ValueError, match="invalid literal for int"):
-            phr.run_initial_from_file("test.pqi", ic_array)
-
+        ic = pd.DataFrame([["a", "b", "c", "d", "e", "f", "g"]])
 
-class TestPhreeqcRMFileOperations:
-    """Test file operation methods."""
+        with pytest.raises(ValueError, match="Initial conditions must be convertible to a numpy array"):
+            phr.run_initial_from_file("test.pqi", ic)
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_file_operations(self, mock_cdll):
-        """Test file open/close operations."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_OpenFiles.return_value = 0
-        mock_lib.RM_CloseFiles.return_value = 0
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_run_initial_from_file_ic_flattened_fortran_order(self, mock_phreeqcrm_cls):
+        """Test that IC array is flattened in Fortran order."""
+        mock_rm = MagicMock()
+        mock_rm.RunFile.return_value = 0
+        mock_rm.GetComponents.return_value = ["H"]
+        mock_rm.GetSpeciesNames.return_value = ["H+"]
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create()
+        phr.create(nxyz=2)
 
-        # Test opening files
-        result = phr.rm_open_files()
-        assert result.code == 0
-        mock_lib.RM_OpenFiles.assert_called_once_with(1)
+        ic = np.array([[1, -1, -1, -1, -1, -1, -1], [2, -1, -1, -1, -1, -1, -1]])
+        phr.run_initial_from_file("test.pqi", ic)
 
-        # Test closing files
-        result = phr.rm_close_files()
-        assert result.code == 0
-        mock_lib.RM_CloseFiles.assert_called_once_with(1)
+        call_args = mock_rm.InitialPhreeqc2Module.call_args[0][0]
+        expected = ic.flatten("F").astype(np.int32)
+        np.testing.assert_array_equal(call_args, expected)
 
 
-class TestPhreeqcRMCleanup:
-    """Test cleanup and destruction methods."""
+class TestPhreeqcRMSelectedOutput:
+    """Test selected output functionality."""
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_destroy(self, mock_cdll):
-        """Test RM_Destroy method."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_Destroy.return_value = 0
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_get_selected_output_df_basic(self, mock_phreeqcrm_cls):
+        """Test get_selected_output_df method basic functionality."""
+        mock_rm = MagicMock()
+        mock_rm.GetSelectedOutputHeadings.return_value = ["pH", "pe"]
+        mock_rm.GetSelectedOutput.return_value = [7.0, 4.0, 8.0, 3.0]
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        result = phr.rm_destroy()
-        assert result.code == 0
-        mock_lib.RM_Destroy.assert_called_once_with(1)
+        phr.create(nxyz=2)
 
+        df = phr.get_selected_output_df()
 
-class TestPhreeqcRMAdditionalMethods:
-    """Test additional methods."""
+        assert isinstance(df, pd.DataFrame)
+        assert list(df.columns) == ["pH", "pe"]
+        assert df.shape == (2, 2)
+        mock_rm.GetSelectedOutputHeadings.assert_called_once()
+        mock_rm.GetSelectedOutput.assert_called_once()
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_error_and_message_methods(self, mock_cdll):
-        """Test error handling and message methods."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_Abort.return_value = 0
-        mock_lib.RM_ErrorMessage.return_value = 0
-        mock_lib.RM_DecodeError.return_value = "Test error message"
-        mock_lib.RM_LogMessage.return_value = 0
-        mock_lib.RM_ScreenMessage.return_value = 0
-        mock_lib.RM_OutputMessage.return_value = 0
-        mock_lib.RM_WarningMessage.return_value = 0
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_get_selected_output_df_values(self, mock_phreeqcrm_cls):
+        """Test get_selected_output_df returns correct values."""
+        mock_rm = MagicMock()
+        mock_rm.GetSelectedOutputHeadings.return_value = ["pH", "pe"]
+        # Data layout: ncolsel values per column, nxyz rows
+        # Column-major: [pH_cell0, pH_cell1, pe_cell0, pe_cell1]
+        mock_rm.GetSelectedOutput.return_value = [7.0, 8.0, 4.0, 3.0]
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create()
-
-        # Test error methods
-        result = phr.rm_abort(1, "Test abort")
-        assert result.code == 0
-
-        result = phr.rm_error_message("Test error")
-        assert result.code == 0
-
-        error_msg = phr.rm_decode_error(1)
-        assert error_msg == "Test error message"
-
-        # Test message methods (these return raw integers)
-        result = phr.rm_log_message("Test log message")
-        assert result == 0
-
-        result = phr.rm_screen_message("Test screen message")
-        assert result == 0
+        phr.create(nxyz=2)
 
-        result = phr.rm_output_message("Test output message")
-        assert result == 0
+        df = phr.get_selected_output_df()
 
-        result = phr.rm_warning_message("Test warning")
-        assert result == 0
+        assert df["pH"].iloc[0] == 7.0
+        assert df["pH"].iloc[1] == 8.0
+        assert df["pe"].iloc[0] == 4.0
+        assert df["pe"].iloc[1] == 3.0
 
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_utility_and_mapping_methods(self, mock_cdll):
-        """Test utility and mapping methods."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_Concentrations2Utility.return_value = 0
-        mock_lib.RM_CreateMapping.return_value = 0
-        mock_lib.RM_GetBackwardMapping.return_value = 0
-        mock_lib.RM_InitialPhreeqc2Concentrations.return_value = 0
-        mock_lib.RM_InitialPhreeqc2SpeciesConcentrations.return_value = 0
-        mock_cdll.return_value = mock_lib
 
-        phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=5)
+class TestPhreeqcRMPublicAttribute:
+    """Test that self.rm is accessible as a public attribute."""
 
-        # Test utility methods
-        conc = np.array([1.0, 2.0, 3.0])
-        result = phr.rm_concentrations2utility(conc, 0, 25.0, 1.0)
-        assert result.code == 0
-
-        # Test mapping methods
-        grid2chem = np.array([0, 1, 2, 3, 4])
-        result = phr.rm_create_mapping(grid2chem)
-        assert result.code == 0
-
-        mapping_list = np.zeros(10, dtype=int)
-        result = phr.rm_get_backward_mapping(0, mapping_list, 10)
-        assert result.code == 0
-
-        # Test initial phreeqc methods (these return raw integers)
-        conc_array = np.zeros(15)  # 5 cells * 3 components
-        boundary_sol1 = np.array([1, 2, 3])
-        boundary_sol2 = np.array([4, 5, 6])
-        fraction1 = np.array([0.5, 0.6, 0.7])
-
-        result = phr.rm_initial_phreeqc2_concentrations(conc_array, 3, boundary_sol1, boundary_sol2, fraction1)
-        assert result == 0  # Raw integer return
-
-        species_conc = np.zeros(50)  # 5 cells * 10 species
-        n_boundary = np.array([3])
-        result = phr.rm_initial_phreeqc2_species_concentrations(
-            species_conc, n_boundary, boundary_sol1, boundary_sol2, fraction1
-        )
-        assert result == 0  # Raw integer return
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_species_and_diffusion_methods(self, mock_cdll):
-        """Test species information and diffusion methods."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_GetSpeciesD25.return_value = 0
-        mock_lib.RM_GetSpeciesLog10Gammas.return_value = 0
-        mock_lib.RM_GetSpeciesZ.return_value = 0
-        mock_lib.RM_SpeciesConcentrations2Module.return_value = 0
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=3)
-
-        # Test species diffusion coefficients
-        species_d25 = np.zeros(10)  # 10 species
-        result = phr.rm_get_species_d25(species_d25)
-        assert result.code == 0
-
-        # Test species activity coefficients
-        species_gammas = np.zeros(30)  # 3 cells * 10 species
-        result = phr.rm_get_species_log10_gammas(species_gammas)
-        assert result.code == 0
-
-        # Test species charges
-        species_z = np.zeros(10)
-        result = phr.rm_get_species_z(species_z)
-        assert result.code == 0
-
-        # Test species concentrations to module transfer
-        species_conc = np.ones(30)  # 3 cells * 10 species
-        result = phr.rm_species_concentrations2_module(species_conc)
-        assert result == 0  # This returns raw integer
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_additional_setter_methods(self, mock_cdll):
-        """Test additional setter methods."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_SetDensity.return_value = 0
-        mock_lib.RM_SetPressure.return_value = 0
-        mock_lib.RM_SetTemperature.return_value = 0
-        mock_lib.RM_SetTimeConversion.return_value = 0
-        mock_lib.RM_UseSolutionDensityVolume.return_value = 0
-        mock_lib.RM_SetCurrentSelectedOutputUserNumber.return_value = 0
-        mock_lib.RM_SetScreenOn.return_value = 0
-        mock_lib.RM_SetSelectedOutputOn.return_value = 0
-        mock_cdll.return_value = mock_lib
+    @patch("mibiremo.phreeqc.phreeqcrm.PhreeqcRM")
+    def test_rm_attribute_accessible(self, mock_phreeqcrm_cls):
+        """Test that self.rm is the underlying PhreeqcRM instance."""
+        mock_rm = MagicMock()
+        mock_phreeqcrm_cls.return_value = mock_rm
 
         phr = mibiremo.PhreeqcRM()
-        phr.create(nxyz=5)
-
-        # Test array setters (these return raw integers, not irm_result)
-        density = np.full(5, 1.0)
-        result = phr.rm_set_density(density)
-        assert result == 0  # Direct return value check
-
-        pressure = np.full(5, 1.0)
-        result = phr.rm_set_pressure(pressure)
-        assert result == 0
-
-        temperature = np.full(5, 25.0)
-        result = phr.rm_set_temperature(temperature)
-        assert result == 0
-
-        # Test scalar setters (these return raw integers, not irm_result)
-        result = phr.rm_set_time_conversion(86400.0)  # seconds to days
-        assert result == 0
-
-        result = phr.rm_use_solution_density_volume(1)
-        assert result == 0
-
-        result = phr.rm_set_current_selected_output_user_number(1)
-        assert result == 0
-
-        result = phr.rm_set_screen_on(1)
-        assert result == 0
-
-        result = phr.rm_set_selected_output_on(1)
-        assert result == 0
-
-    @patch("mibiremo.phreeqc.ctypes.CDLL")
-    def test_get_methods_simple(self, mock_cdll):
-        """Test simple getter methods that return integers."""
-        mock_lib = MagicMock()
-        mock_lib.RM_Create.return_value = 1
-        mock_lib.RM_GetExchangeSpeciesCount.return_value = 5
-        mock_lib.RM_GetGasComponentsCount.return_value = 3
-        mock_lib.RM_GetKineticReactionsCount.return_value = 2
-        mock_lib.RM_GetMpiMyself.return_value = 0
-        mock_lib.RM_GetMpiTasks.return_value = 1
-        mock_lib.RM_GetNthSelectedOutputUserNumber.return_value = 1
-        mock_lib.RM_GetSelectedOutputCount.return_value = 1
-        mock_lib.RM_GetSICount.return_value = 4
-        mock_lib.RM_GetSolidSolutionComponentsCount.return_value = 2
-        mock_lib.RM_GetTimeConversion.return_value = 86400.0
-        mock_lib.RM_GetErrorStringLength.return_value = 50
-        mock_cdll.return_value = mock_lib
-
-        phr = mibiremo.PhreeqcRM()
-        phr.create()
+        phr.create(nxyz=1)
 
-        # Test getter methods
-        assert phr.rm_get_exchange_species_count() == 5
-        assert phr.rm_get_gas_components_count() == 3
-        assert phr.rm_get_kinetic_reactions_count() == 2
-        assert phr.rm_get_mpi_myself() == 0
-        assert phr.rm_get_mpi_tasks() == 1
-        assert phr.rm_get_nth_selected_output_user_number(0) == 1
-        assert phr.rm_get_selected_output_count() == 1
-        assert phr.rm_get_si_count() == 4
-        assert phr.rm_get_solid_solution_components_count() == 2
-        assert phr.rm_get_time_conversion() == 86400.0
-        assert phr.rm_get_error_string_length() == 50
+        assert phr.rm is mock_rm
+        # Users can call methods directly on phr.rm
+        phr.rm.SetTime(100.0)
+        mock_rm.SetTime.assert_called_once_with(100.0)