diff --git a/examples/heat_simple_autodiff_grad.jl b/examples/heat_simple_autodiff_grad.jl
index e3716a22c451193cafd3e9e028e505faf4ec8852..3c5d86814234960b7c450cfc1c1d627e9f09058c 100644
--- a/examples/heat_simple_autodiff_grad.jl
+++ b/examples/heat_simple_autodiff_grad.jl
@@ -8,7 +8,8 @@ using CryoGrid
 # Set up forcings and boundary conditions similarly to other examples:
 forcings = loadforcings(CryoGrid.Forcings.Samoylov_ERA_obs_fitted_1979_2014_spinup_extended_2044);
 soilprofile, tempprofile = CryoGrid.SamoylovDefault
-soilprofile = SoilProfile(0.0u"m" => SimpleSoil())
+freezecurve = PainterKarra(swrc=VanGenuchten())
+soilprofile = SoilProfile(0.0u"m" => SimpleSoil(; freezecurve))
 grid = CryoGrid.DefaultGrid_5cm
 initT = initializer(:T, tempprofile)
 tile = CryoGrid.SoilHeatTile(
@@ -43,10 +44,10 @@ using OrdinaryDiffEq
 using Statistics
 function loss(prob::CryoGridProblem, p)
     newprob = remake(prob, p=p)
-    # Here we specify the sensitivity algorithm. Note that this is only
-    # necessary for reverse-mode autodiff with Zygote.
-    # autojacvec = true uses ForwardDiff to calculate the jacobian;
-    # enabling checkpointing (theoretically) reduces the memory cost of the backwards pass.
+    ## Here we specify the sensitivity algorithm. Note that this is only
+    ## necessary for reverse-mode autodiff with Zygote.
+    ## autojacvec = true uses ForwardDiff to calculate the jacobian;
+    ## enabling checkpointing (theoretically) reduces the memory cost of the backwards pass.
     sensealg = InterpolatingAdjoint(autojacvec=true, checkpointing=true)
     newsol = solve(newprob, Euler(), dt=300.0, sensealg=sensealg);
     newout = CryoGridOutput(newsol)
@@ -57,8 +58,8 @@ end
 pvec = vec(prob.p)
 fd_grad = @time ForwardDiff.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
 
-# We can also try with reverse-mode autodiff. This is generally slower for smaller numbers
-# of parmaeters (<100) but could be worthwhile for model configurations with high-dimensional
-# parameterizations.
-zy_grad = @time Zygote.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
-@assert maximum(abs.(fd_grad .- zy_grad)) .< 1e-6 "Forward and reverse gradients don't match!"
+## We can also try with reverse-mode autodiff. This is generally slower for smaller numbers
+## of parmaeters (<100) but could be worthwhile for model configurations with high-dimensional
+## parameterizations.
+## zy_grad = @time Zygote.gradient(páµ¢ -> loss(prob, páµ¢), pvec)
+## @assert maximum(abs.(fd_grad .- zy_grad)) .< 1e-6 "Forward and reverse gradients don't match!"