## 2 Materials and Methods
### Methods
#### 2.2.3 Hydration Studies
However, due to the primarily amorphous structure of the slags, determination of their DoR using the Rietveld analysis results was not possible. As such, SEM image analysis was used for estimating the slag DoR over time as a means to evaluate their reactivity. The percentage of slag surface area in the images at a certain age can be compared to its initial value, which can then be used to estimate its DoR. One hydrated paste slice was dedicated to SEM imaging at each age. For each slag, SEM images were made after 7, 28 and 91 days of hydration. The hydration-stopped slices were impregnated under vacuum using Epofix resin. Impregnated specimens were removed from the mould after 24 h of hardening. The specimens were polished (using a water-free lubricant) with a 9 \(\upmu\)m abrasive until the specimen was uncovered while making sure not to expose the non-impregnated portion. Then, the specimens were polished for 2-10 min at 3 and 1 \(\upmu\)m until all scratches were removed. The polished surfaces were exposed to scanning electron microscopy, where energy dispersive spectrometry (EDS) and back-scattered electron (BSE) images were acquired and analysed to determine the slag DoR over time. Image data were acquired using FEI FEG Nova NanoSEM 450 equipped with a Bruker XFlash 5030 detector. SEM BSE and EDS mappings were acquired at 500x magnification at an electron acceleration voltage of 20 kV. For each specimen, 25 or 100 BSE \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paste Label** & **Slag Source** & \multicolumn{3}{c}{**Mixing Ratios (wt./wt.)**} \\ \hline  & & **Water-to-Binder** & **Slag-to-Binder** & **Quartz-to-Binder** \\ \hline P\_PC & – & 0.4 & 0.0 & 0.0 \\ P\_QP & – & 0.4 & 0.0 & 0.3 \\ P\_TS1 & TS1: Treated S1 & & & \\ P\_TS2 & TS2: Treated S2 & 0.4 & 0.3 & 0.0 \\ P\_TS3 & TS3: Treated S3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mixing proportions and batching values of paste mixtures. images were taken in a raster of 5 \(\times\) 5 or 10 \(\times\) 10, depending on slag type and particle size, such that the images were representative for the sample. The original dimensions of the images were 1536\(\times\)1024 pixels and were cropped to 1498 \(\times\) 952 pixels to remove any overlaps on the edges of the neighbouring images. For each specimen, 2-4 EDS mappings were taken, resulting in 21 mappings in total. EDS mappings containing Mg, Al, Si and Ca were combined with BSE images and converted to a colour image. Figure 2a,b shows a sample BSE image and a combined BSE+EDS image, respectively. In Figure 2b, the colours red, green and blue indicate Si, Mg and Ca, respectively. Regions containing both Si and Mg are shown as orange. The images were processed using a procedure that applies neural networks with the aim of segmentation such that all slag particles are selected without selecting other phases/areas. The model used was a U-net developed by [PERSON] et al. (2015) [PERSON] et al. (2015). To create training data for training the model, the 21 combined EDS+BSE images were manually segmented using thresholds and filters as follows. First, a threshold was applied to the BSE images to select only the brightest phases, and the noise was removed. This selected mainly the slag and cluster phases. Next, for each selected particle, the average value of the EDS  
Figure 2: Sample raw and processed images. (**a**) BSE image, (**b**) combined BSE+EDS image and (**c**) segmented image. mappings was calculated and a K-means algorithm was applied to select the cluster of EDS values that consisted of slag. Then, the segmented images were manually adapted to come to an optimal segmentation. Figure 2c shows a sample segmented image. The resulting segmentations were paired with the respective BSE images and used for training the model. Nineteen BSE images were used for training the model and two for testing. Training was carried out by transfer learning for 300 epochs on one NVIDIA GeForce RTX 2080 Ti GPU. The model was then applied to the test set.