diff --git a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
index e97e981..058a206 100644
--- a/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
+++ b/python/energy_flow_proper/10_antizeno_engine/10_first_anti_zeno.py
@@ -20,7 +20,7 @@ import logging
 logging_setup(logging.INFO, show_stocproc=False)
 
 def anti_zeno_engine(
-    Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3
+    Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3, detune = 0
 ):
     # τ_bath = 1 / ω_c
     τ_mod = 2 * np.pi / Δ
@@ -36,7 +36,7 @@ def anti_zeno_engine(
 
     cycles = int(np.around(τ_bath / τ_mod * cycle_scale))
     τ_c = cycles * τ_mod
-    detune = 0
+
     H_mat = 1 / 2 * (qt.sigmaz().full()) * ω_0
     Δ_switch = τ_mod * switch_cycles
     t_max = n * (τ_c + τ_off)
@@ -82,7 +82,7 @@ def anti_zeno_engine(
 
     return model, (τ_mod, τ_c, τ_bath, cycles, model.ω_s)
 
-model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2)
+model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2, detune=.5, ω_0=6.5)
 params
 
 
diff --git a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
index 8af1578..5857db8 100644
--- a/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
+++ b/python/energy_flow_proper/10_antizeno_engine/anti_zeno_engine.org
@@ -37,7 +37,7 @@ Init ray and silence stocproc.
 * Model Definition
 #+begin_src jupyter-python :results none
   def anti_zeno_engine(
-      Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3
+      Δ=5, γ=0.1 / 2, ω_c=1, ω_0=6, ε=1e-3, ε_couple=1 / 2, n=6, cycle_scale=1, switch_cycles=3, detune = 0
   ):
       # τ_bath = 1 / ω_c
       τ_mod = 2 * np.pi / Δ
@@ -53,7 +53,7 @@ Init ray and silence stocproc.
 
       cycles = int(np.around(τ_bath / τ_mod * cycle_scale))
       τ_c = cycles * τ_mod
-      detune = 0
+
       H_mat = 1 / 2 * (qt.sigmaz().full()) * ω_0
       Δ_switch = τ_mod * switch_cycles
       t_max = n * (τ_c + τ_off)
@@ -103,12 +103,12 @@ Init ray and silence stocproc.
 
 * Model Definition
 #+begin_src jupyter-python
-  model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2)
+  model, params = anti_zeno_engine(ε=1/2, ε_couple=1e-10, n=2, detune=.5, ω_0=6.5)
   params
 #+end_src
 
 #+RESULTS:
-| 1.2566370614359172 | 348.08846601774906 | 347.6950495572053 | 277 | (0 10) |
+| 1.2566370614359172 | 348.08846601774906 | 347.6950495572053 | 277 | (0.0 11.0) |
 
 Let's test the assumptions of the paper.
 
@@ -128,8 +128,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f9607180cd0> |
-[[file:./.ob-jupyter/103f059a370864a94b9d48d67a3dbf9e93399722.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f96068a9340> |
+[[file:./.ob-jupyter/c416c29dc1b7efcfe7db368831188c25e55c74a6.svg]]
 :END:
 
 #+begin_src jupyter-python :tangle nil
@@ -140,8 +140,8 @@ Let's test the assumptions of the paper.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f96069872e0> |
-[[file:./.ob-jupyter/160f075c49948f61a1e0d3b9dc1e386d30f3543d.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f96069d2340> |
+[[file:./.ob-jupyter/ecf0b1db51e0d2c424e05f4cd2ec33d9ceff13d7.svg]]
 :END:
 
 
@@ -273,6 +273,7 @@ Let's test the assumptions of the paper.
 :END:
 
 - **too fast decoupling kills it**
+- no anti-zeno effects without detuning?
 - **spectral separation seems to be important** or maybe it's just the coupling strenght
 - too long modulation kills it
 
@@ -327,7 +328,7 @@ We need the time points where we sample the total energy.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/66981899ba65d026196d29f54e45b3663b179d43.svg]]
+[[file:./.ob-jupyter/3236f7b1181a4feca36cc963453861545149ff47.svg]]
 
 #+begin_src jupyter-python
   with aux.get_data(model) as data:
@@ -408,3 +409,5 @@ model, params = anti_zeno_engine(ε=1e-1, ε_couple=1/4)
 #+begin_src jupyter-python
   model, params = anti_zeno_engine(ε=1e-1, ε_couple=1e-3, n=3)
 #+end_src
+
+#+RESULTS: