diff --git a/examples/advanced_ODEs.jl b/examples/advanced_ODEs.jl
new file mode 100644
index 00000000..67fd681a
--- /dev/null
+++ b/examples/advanced_ODEs.jl
@@ -0,0 +1,267 @@
+### A Pluto.jl notebook ###
+# v0.20.3
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ 59243782-5b91-4afd-ab68-5283948781d3
+using Pkg; Pkg.activate("..")
+
+# ╔═╡ 27bd53ba-1ea7-11f0-3d13-65f334bf4250
+using PlutoUI, Plots
+
+# ╔═╡ b2f9ff92-f75f-479c-8d87-c34719661b3c
+using Catalyst, DifferentialEquations
+
+# ╔═╡ 3ebf762f-6e72-4811-8884-bf7ba98321e1
+using ModelingToolkit
+
+# ╔═╡ 92b24c3b-9a44-43aa-a446-998e2a1ed4c4
+md"""
+# Modelling mass and temperature changes
+
+Given a degradation reaction 
+
+`C --> ∅`
+
+which has a kinetics of:
+
+$$C' = - k C$$
+
+that is temperature-dependent, with a reaction rate given by the *Arrhenius equation*:
+
+$$k(T) = A e^{-\frac{E_a}{RT}}$$
+
+Suppose our temperature is not constant but follows *Newton's cooling law*:
+
+$$T' = - r (T-T_e)$$
+
+How can we model this using our Julia tools?
+"""
+
+# ╔═╡ bf413087-6f78-47ba-bb8b-e33514ee1cfa
+md"""
+## 1. Abusing Catalyst.jl
+
+Without using anything new, we can implement cooling as a first-order decay of the temperature difference. Note that we use `@observables` to keep track of the true temperature!
+"""
+
+# ╔═╡ 023cfc5d-6e2e-425e-a1fc-edb223e01589
+sys1 = @reaction_network begin
+	@observables T ~ Te + ΔT
+	# ΔT = T - Te
+	A * exp(-Ea/R/(Te+ΔT)), C --> 0
+	r, ΔT --> 0
+end
+
+# ╔═╡ 134c7a48-01d1-4313-a9fe-157b7dab2a17
+convert(ODESystem, sys1)
+
+# ╔═╡ bc67c9d8-ae53-4195-9f35-5299f3dd7941
+md"## 2. Decoupling
+
+see [here](https://docs.sciml.ai/Catalyst/stable/model_creation/functional_parameters/)
+
+Here, the temperature is independent of the concentration; we can solve for T (linear first-order ODE) and plug in a catalyst model. It requires a bit of computation and will fail when were have a more complex model.
+"
+
+# ╔═╡ 306f3a04-f53c-486f-8656-29ecfa56e40d
+import ModelingToolkit: t  # import time
+
+# ╔═╡ 3f2f4599-6b5e-46c8-84f9-dea9bb0183a5
+md" ## 3. Using DifferentialEquations
+
+Catalyst is not ideal for things beyond mass balances; you can just implement the ODE system directly.
+
+This is precisely the same thing, though we build the ODE system here manually rather than automatically.
+"
+
+# ╔═╡ 509a36ed-fcd2-43ae-8768-f1b39f94a34d
+function chemcooling!(du, u, (A, Ea, R, Te, r), t)
+	C, T = u
+	du[1] = dC = - A * exp(-Ea / R / T) * C
+	du[2] = dT = - r * (T - Te)
+end
+
+# ╔═╡ b3c3bbe6-ee7f-40b8-be92-116eb1043368
+md"""
+## 4. Adding an extra equation using MTK
+
+The most advanced and powerful way is adding additional equations to the system. This is the true scalable way. We can combine ODEs with reaction systems to create wildly complex models automatically.
+
+see [here](https://docs.sciml.ai/Catalyst/stable/model_creation/constraint_equations/#constraint_equations_coupling_constraints)
+"""
+
+# ╔═╡ f9294388-e61d-4888-8548-dd6a1811944f
+D = default_time_deriv()  # time derivative operator
+
+# ╔═╡ 5fc2cf63-5bfa-49b9-acd9-b972bc5fbaee
+sys4 = @reaction_network begin
+	A * exp(-Ea / (R * T)), C --> 0
+end
+
+# ╔═╡ 63a4d6d6-d339-46a6-a728-931f015a997f
+#sys4 = @reaction_network begin
+#	$k, C --> 0   # alternative, splice in the code for k
+#end
+
+# ╔═╡ 01ea3432-a53f-4f16-9f13-cb5fa0245d08
+md"## Parameters"
+
+# ╔═╡ ee9550cf-4d00-4fd8-80cc-fe9cbf49e166
+r = 0.018  # cooling rate
+
+# ╔═╡ 25aaf503-95b3-44c4-a270-88b987ad1371
+R = 8.314  # gas cst
+
+# ╔═╡ ad3f22b6-a1a9-40c0-868a-3a6a63bea504
+A = 0.17
+
+# ╔═╡ 0e90bb09-0ae9-43d3-9d19-fe89f1d743ad
+Ea = 89.3
+
+# ╔═╡ 0eeafb45-5bbb-43ca-9cb3-a4ebe06dc64f
+k_temp(T) = A * exp(-Ea / (R * T))
+
+# ╔═╡ fac4b66e-533a-413f-8fbb-cf312244c10b
+plot(k_temp, 20, 100, xlab="T [degree Celsius]", ylab="reaction rate", label="k")
+
+# ╔═╡ 8952f810-79f8-42ec-948e-b78f847c154d
+C0 = 50.0  # initial concentration
+
+# ╔═╡ ec87305a-87ad-403b-a6a5-827f6c9f80e7
+tspan = (0, 60)
+
+# ╔═╡ a90c6b66-2cd7-4bd4-be1b-5a75a0088013
+T0 = 100  # initial temperature
+
+# ╔═╡ 61bce0a0-1a76-472c-886e-fc48c109df46
+@variables T(t) = T0  # define temperature (function of time)
+
+# ╔═╡ 1ecfbbda-f9a2-4382-b7f2-b8fbf1813419
+k =  A * exp(-Ea / (R * T))  # this is an equation that uses the variable T
+
+# ╔═╡ 90ad7025-500f-4526-8379-2aa0bf41b949
+Te = 21  # env temp
+
+# ╔═╡ 9b249ef1-eff5-41a3-aa93-3683560356a5
+temperature(t) = (T0 - Te) * exp(-r*t) + Te
+
+# ╔═╡ f57e5c92-aa19-4811-82ce-991ffa935374
+plot(temperature, 0, 50, xlab="t", ylab="T")
+
+# ╔═╡ 74876f57-8b41-4437-a9eb-4697e8cd6f53
+# rather than model C & T, we eliminate T and make
+# the reaction rate depend on the time
+sys2 = @reaction_network begin
+	A * exp(-Ea/R/(temperature(t))), C --> 0
+end
+
+# ╔═╡ 07e62e76-4271-415c-bfe4-cbf98f409208
+odesys2 = ODEProblem(sys2, [:C => C0], tspan, [:A=>A, :R=>R, :Ea=>Ea]);
+
+# ╔═╡ e5390072-ddfa-483d-9bbc-dbbf56a41480
+sol2 = solve(odesys2);
+
+# ╔═╡ 86f46366-9e6c-4315-898f-01480d937320
+plot(sol2)
+
+# ╔═╡ 7c0adb22-7daf-4110-9378-687843771cef
+sys3 = ODEProblem(chemcooling!, [C0, T0], tspan, (A, Ea, R, Te, r));
+
+# ╔═╡ 33228136-4e19-451d-96d8-c90eef259330
+sol3 = solve(sys3);
+
+# ╔═╡ 53cba252-77f9-463c-9f04-440a5bf7eba1
+plot(sol3, label=["C" "T"])
+
+# ╔═╡ 4c0c885e-6450-4329-9d4c-32d1b68dc687
+newton_cooling_law = (D(T) ~ - r * (T - Te)) 
+
+# ╔═╡ 8bd4b6e7-c7e6-4156-83d1-40aa0f6717d5
+@named osys = ODESystem(newton_cooling_law, t)
+
+# ╔═╡ 1295d9fc-f2ba-46a1-86e5-cf093fe31533
+begin
+	@named sys_with_cooling = extend(osys, sys4)
+	sys_with_cooling = complete(sys_with_cooling)
+end
+
+# ╔═╡ 974311a5-515c-4dcf-bc05-63f4a2c85553
+convert(ODESystem, sys_with_cooling)
+
+# ╔═╡ 4b4e1bb0-d753-4c2e-aac4-ecf564be404f
+odesys4 = ODEProblem(sys_with_cooling, [:C=>C0, :T=>T0], tspan, [:A=>A, :R=>R, :Ea=>Ea]);
+
+# ╔═╡ eff6e2ff-99ad-4a82-8881-3f525d28c39e
+sol4 = solve(odesys4);
+
+# ╔═╡ 7f6a4cba-6656-4098-9852-3865cc68b70e
+plot(sol4)
+
+# ╔═╡ 5f0b6722-35bd-4818-be4e-9b79895ca509
+ΔT0 = T0 - Te
+
+# ╔═╡ 82cb2040-3b2f-4599-ab93-0b9fca9eee3b
+odesys1 = ODEProblem(sys1, [:C => C0, :ΔT=>ΔT0], tspan, [:A=>A, :r=>r, :R=>R, :Ea=>Ea, :Te=>Te]);
+
+# ╔═╡ 6e072039-2f16-478b-99c4-207505e85397
+sol1 = solve(odesys1);
+
+# ╔═╡ b3e54b72-4ab6-42c9-afda-ccb61844fa54
+plot(sol1, idxs=[:C, :T])
+
+# ╔═╡ f39e513d-f2ab-45fd-9621-fa893c793c47
+TableOfContents()
+
+# ╔═╡ Cell order:
+# ╠═27bd53ba-1ea7-11f0-3d13-65f334bf4250
+# ╠═59243782-5b91-4afd-ab68-5283948781d3
+# ╟─92b24c3b-9a44-43aa-a446-998e2a1ed4c4
+# ╠═0eeafb45-5bbb-43ca-9cb3-a4ebe06dc64f
+# ╠═fac4b66e-533a-413f-8fbb-cf312244c10b
+# ╟─bf413087-6f78-47ba-bb8b-e33514ee1cfa
+# ╠═b2f9ff92-f75f-479c-8d87-c34719661b3c
+# ╠═023cfc5d-6e2e-425e-a1fc-edb223e01589
+# ╠═134c7a48-01d1-4313-a9fe-157b7dab2a17
+# ╠═82cb2040-3b2f-4599-ab93-0b9fca9eee3b
+# ╠═6e072039-2f16-478b-99c4-207505e85397
+# ╠═b3e54b72-4ab6-42c9-afda-ccb61844fa54
+# ╟─bc67c9d8-ae53-4195-9f35-5299f3dd7941
+# ╠═306f3a04-f53c-486f-8656-29ecfa56e40d
+# ╠═9b249ef1-eff5-41a3-aa93-3683560356a5
+# ╠═f57e5c92-aa19-4811-82ce-991ffa935374
+# ╠═74876f57-8b41-4437-a9eb-4697e8cd6f53
+# ╠═07e62e76-4271-415c-bfe4-cbf98f409208
+# ╠═e5390072-ddfa-483d-9bbc-dbbf56a41480
+# ╠═86f46366-9e6c-4315-898f-01480d937320
+# ╟─3f2f4599-6b5e-46c8-84f9-dea9bb0183a5
+# ╠═509a36ed-fcd2-43ae-8768-f1b39f94a34d
+# ╠═7c0adb22-7daf-4110-9378-687843771cef
+# ╠═33228136-4e19-451d-96d8-c90eef259330
+# ╠═53cba252-77f9-463c-9f04-440a5bf7eba1
+# ╟─b3c3bbe6-ee7f-40b8-be92-116eb1043368
+# ╠═3ebf762f-6e72-4811-8884-bf7ba98321e1
+# ╠═f9294388-e61d-4888-8548-dd6a1811944f
+# ╠═61bce0a0-1a76-472c-886e-fc48c109df46
+# ╠═4c0c885e-6450-4329-9d4c-32d1b68dc687
+# ╠═8bd4b6e7-c7e6-4156-83d1-40aa0f6717d5
+# ╠═1ecfbbda-f9a2-4382-b7f2-b8fbf1813419
+# ╠═5fc2cf63-5bfa-49b9-acd9-b972bc5fbaee
+# ╠═63a4d6d6-d339-46a6-a728-931f015a997f
+# ╠═1295d9fc-f2ba-46a1-86e5-cf093fe31533
+# ╠═974311a5-515c-4dcf-bc05-63f4a2c85553
+# ╠═4b4e1bb0-d753-4c2e-aac4-ecf564be404f
+# ╠═eff6e2ff-99ad-4a82-8881-3f525d28c39e
+# ╠═7f6a4cba-6656-4098-9852-3865cc68b70e
+# ╟─01ea3432-a53f-4f16-9f13-cb5fa0245d08
+# ╠═ee9550cf-4d00-4fd8-80cc-fe9cbf49e166
+# ╠═25aaf503-95b3-44c4-a270-88b987ad1371
+# ╠═ad3f22b6-a1a9-40c0-868a-3a6a63bea504
+# ╠═0e90bb09-0ae9-43d3-9d19-fe89f1d743ad
+# ╠═8952f810-79f8-42ec-948e-b78f847c154d
+# ╠═ec87305a-87ad-403b-a6a5-827f6c9f80e7
+# ╠═a90c6b66-2cd7-4bd4-be1b-5a75a0088013
+# ╠═90ad7025-500f-4526-8379-2aa0bf41b949
+# ╠═5f0b6722-35bd-4818-be4e-9b79895ca509
+# ╠═f39e513d-f2ab-45fd-9621-fa893c793c47
diff --git a/examples/censored.jl b/examples/censored.jl
index d7250b9c..67d19b9e 100644
--- a/examples/censored.jl
+++ b/examples/censored.jl
@@ -12,13 +12,12 @@ md"""
 
 # Censored
 
-Researchers are studying a new drug designed to cure a specific type of skin condition. They monitor 15 patients undergoing treatment for five months, recording the time it takes for patients to show no signs of the condition. Of the 15, seven were cured in the five-month study period, at `[2.1, 4.7, 1.6, 2.8, 4.3, 1.9, 4.2]` months. Eight patients were not achieve cured within the five-month study period (this is censored data). You may assume that these persons were cured at some later, unspecified time after the five-month trial. Given a Gamma prior on the rate parameter $\lambda$, what is its posterior distribution? 
+Researchers are studying a new drug designed to cure a specific type of skin condition. They monitor 15 patients undergoing treatment for five months, recording the time it takes for patients to show no signs of the condition. Of the 15, seven were cured in the five-month study period, at `[2.1, 4.7, 1.6, 2.8, 4.3, 1.9, 4.2]` months. These data are Exponentially distributed with a rate parameter $\lambda$ ($E[X] = 1/\lambda$). Eight patients were not cured within the five-month study period (this is censored data). You may assume that these persons were cured at some later, unspecified time after the five-month trial. What is its posterior distribution given a Gamma prior on the rate parameter $\lambda$? 
 
 Give a 95% credibility interval.
 
-Given the posterior of $\lambda$, what is the chance someone cures within 10 months?
+Given the distribution posterior of $\lambda$, what is the chance someone is cured within 10 months?
 
-Hint: For the censored data, can you compute the probability that a patient survives beyond five months? What type of distribution models 8 of 15 patients exceeding this time?
 """
 
 # ╔═╡ 3e8810ff-b456-473a-88e1-3344278ee78f
@@ -30,13 +29,13 @@ n = 8  # not cured in 5 months
 # ╔═╡ 4fc7936b-aafc-46a0-8b58-4a601d514586
 @model function censored(x, n, tend)
 	λ ~ Gamma()  # rate paramters
-    N = length(x) + n  # total number of 
-    psurvive = 1 - cdf(Exponential(1/λ), tend)
-    n ~ Binomial(N, psurvive)
+    N = length(x) + n  # total number of participants
+    p = 1 - cdf(Exponential(1/λ), tend)  # chance of being cured after tend
+    n ~ Binomial(N, p)  # number of patient not cured within tend
     for i in 1:length(x)
-        x[i] ~ Exponential(1/λ)
+        x[i] ~ Exponential(1/λ)  # recovery time before tend
     end
-	return cdf(Exponential(1/λ), 10)
+	return cdf(Exponential(1/λ), 10)  
 end
 
 # ╔═╡ 96419a33-6a13-46d2-a610-55b29737eb7b
@@ -56,17 +55,33 @@ posterior_chain = sample(model, NUTS(), 10_000);
 summarize(posterior_chain)
 
 # ╔═╡ eea303cc-b1fa-42a4-9eed-ed5e608ff931
-quantile(posterior_chain, q=[0.025, 0.975])  # 95% credibility interval
+quantile(posterior_chain, q=[0.025, 0.975])  # 95% credibility interval rate par
+
+# ╔═╡ b27e5bf2-b0c2-4c8a-a46c-c4a5fad4411a
+Erecovery = inv.(posterior_chain[:λ])
+
+# ╔═╡ e3622cdb-6e56-4cbd-b9c4-590a90ccbac4
+# 95% credibility interval expected recovery time
+quantile(Erecovery, [0.025, 0.975])
 
 # ╔═╡ e4a44333-2216-4941-bee1-3befe8325c51
 p10months = generated_quantities(model, posterior_chain)
 
+# ╔═╡ 3e0459d7-c623-4a35-851b-164759e85548
+histogram(p10months)
+
 # ╔═╡ d34a22c4-146d-414e-9c57-219defad60cd
 mean(p10months)  # expected posterior probability that one is cured in 10 months
 
 # ╔═╡ 55a93fd2-7cb8-4ceb-b90d-0f533207045e
 quantile(p10months, [0.025, 0.975])  # 95% credibility interval
 
+# ╔═╡ 0cc74646-c899-437a-9e21-824d4c702d63
+hint(text) = Markdown.MD(Markdown.Admonition("hint", "Hint", [text]));
+
+# ╔═╡ 061160a8-62af-4af4-9d1a-7e22e236969c
+hint(md"For the censored data, can you compute the probability that a patient survives beyond five months? What type of distribution models 8 of 15 patients exceeding this time?")
+
 # ╔═╡ 00000000-0000-0000-0000-000000000001
 PLUTO_PROJECT_TOML_CONTENTS = """
 [deps]
@@ -2552,7 +2567,8 @@ version = "1.4.1+1"
 
 # ╔═╡ Cell order:
 # ╠═17b60ab6-b21e-11ef-07cf-d7fb167d8d72
-# ╟─6413a973-95a9-4525-999a-8570b957d4f8
+# ╠═6413a973-95a9-4525-999a-8570b957d4f8
+# ╟─061160a8-62af-4af4-9d1a-7e22e236969c
 # ╠═3e8810ff-b456-473a-88e1-3344278ee78f
 # ╠═efe76607-7c83-4c1a-bfec-84fb90c44154
 # ╠═4fc7936b-aafc-46a0-8b58-4a601d514586
@@ -2562,8 +2578,12 @@ version = "1.4.1+1"
 # ╠═30bf124d-7d9b-48bb-b8e5-80beecc8f839
 # ╠═e7672833-0044-40e9-a215-b3c51c4ca8d4
 # ╠═eea303cc-b1fa-42a4-9eed-ed5e608ff931
+# ╠═b27e5bf2-b0c2-4c8a-a46c-c4a5fad4411a
+# ╠═e3622cdb-6e56-4cbd-b9c4-590a90ccbac4
 # ╠═e4a44333-2216-4941-bee1-3befe8325c51
+# ╠═3e0459d7-c623-4a35-851b-164759e85548
 # ╠═d34a22c4-146d-414e-9c57-219defad60cd
 # ╠═55a93fd2-7cb8-4ceb-b90d-0f533207045e
+# ╟─0cc74646-c899-437a-9e21-824d4c702d63
 # ╟─00000000-0000-0000-0000-000000000001
 # ╟─00000000-0000-0000-0000-000000000002
diff --git a/src/exercises/ode_model_catalyst_intro.jl b/src/exercises/ode_model_catalyst_intro.jl
index 4eebdb19..82c5760b 100644
--- a/src/exercises/ode_model_catalyst_intro.jl
+++ b/src/exercises/ode_model_catalyst_intro.jl
@@ -223,7 +223,7 @@ osys = convert(ODESystem, infection_model)
 
 # ╔═╡ eeb25e50-165b-4e93-8397-5b09fe8e7242
 md"""
-Note that the model equations are essencially:
+Note that the model equations are essentially:
 
 $$\cfrac{dS(t)}{dt} = -\alpha \beta S(t) I(t)$$
 $$\cfrac{dI(t)}{dt} = \alpha \beta S(t) I(t) - r I(t)$$