diff --git a/notes/sections/3.md b/notes/sections/3.md
index ba5a456..dae1426 100644
--- a/notes/sections/3.md
+++ b/notes/sections/3.md
@@ -43,7 +43,7 @@ The proper distribution of $\theta$ is obtained by applying a transform
 to a variable $x$ with uniform distribution in $[0, 1)$,
 easily generated by the GSL function `gsl_rng_uniform()`.
 
-<div id="fig:compare">
+::: { id=fig:compare }
   ![Uniformly distributed points with $\theta$ evenly distributed between
   0 and $\pi$.](images/3-histo-i-u.pdf){width=50%}
   ![Points uniformly distributed on a spherical
@@ -56,7 +56,7 @@ easily generated by the GSL function `gsl_rng_uniform()`.
 
 Examples of samples. On the left, points with $\theta$ evenly distributed
 between 0 and $\pi$; on the right, points with $\theta$ properly distributed.
-</div>
+:::
 
 The transformation can be found by imposing the angular PDF to be a constant:
 \begin{align*}
diff --git a/notes/sections/6.md b/notes/sections/6.md
index 54d0eed..de01c77 100644
--- a/notes/sections/6.md
+++ b/notes/sections/6.md
@@ -594,7 +594,7 @@ amplitude changes: it always gives the same outcome, which would be exactly the
 original signal, if the floating point precision would not affect the result. In
 fact, the FFT is the analytical result of the deconvolution.
 
-<div id="fig:rounds-noiseless">
+::: {id=fig:rounds-noiseless}
   ![](images/6-nonoise-rounds-0.1.pdf){#fig:rless-0.1}
 
   ![](images/6-nonoise-rounds-0.5.pdf){#fig:rless-0.5}
@@ -604,9 +604,9 @@ fact, the FFT is the analytical result of the deconvolution.
 EMD as a function of RL rounds for different kernel $\sigma$ values. The
 average is shown in red and the standard deviation in grey. Noiseless results
 shown.
-</div>
+:::
 
-<div id="fig:emd-noiseless">
+::: {id="fig:emd-noiseless"}
   ![$\sigma = 0.1 \, \Delta \theta$](images/6-nonoise-emd-0.1.pdf){#fig:eless-0.1}
 
   ![$\sigma = 0.5 \, \Delta \theta$](images/6-nonoise-emd-0.5.pdf){#fig:eless-0.5}
@@ -617,7 +617,7 @@ EMD distributions for different kernel $\sigma$ values. The plots on the left
 show the results for the FFT deconvolution, the central column the results for
 the RL deconvolution and the third one shows the EMD for the convolved signal.
 Noiseless results.
-</div>
+:::
 
 For this reason, the EMD obtained with the FFT can be used as a reference point
 against which to compare the EMDs measured with RL.  
@@ -706,7 +706,7 @@ However, in real world applications the measures are affected by (possibly
 unknown) noise and the signal can only be partially reconstructed by either
 method.
 
-<div id="fig:rounds-noise">
+::: {id=fig:rounds-noise}
   ![](images/6-noise-rounds-0.005.pdf){#fig:rnoise-0.005}
                            
   ![](images/6-noise-rounds-0.01.pdf){#fig:rnoise-0.01}
@@ -716,9 +716,9 @@ method.
 EMD as a function of RL rounds for different noise $\sigma_N$ values with the
 kernel $\sigma = 0.8 \Delta \theta$. The average is shown in red and the
 standard deviation in grey. Noisy results.
-</div>
+:::
 
-<div id="fig:emd-noisy">
+::: {id=fig:emd-noisy}
   ![$\sigma_N = 0.005$](images/6-noise-emd-0.005.pdf){#fig:enoise-0.005}
 
   ![$\sigma_N = 0.01$](images/6-noise-emd-0.01.pdf){#fig:enoise-0.01}
@@ -729,4 +729,4 @@ EMD distributions for different noise $\sigma_N$ values.  The plots on the left
 show the results for the FFT deconvolution, the central column the results for
 the RL deconvolution and the third one shows the EMD for the convolved signal.
 Noisy results.
-</div>
+:::