## 3 Results and Discussion  
### Surface Morphology and Fiber Distribution Investigations by Scanning Electron Microscopy  
In order to explore the components as well as the structure of the samples with the resins, scanning electron microscopy analysis was performed. [PERSON] et al. [40] highlight that electron microscopy produces significant results for mineral wool insulations. They have highlighted that resin can increase the density and can enhance the thermal and mechanical properties. Furthermore, they measured the fiber diameter to be 6-8 um with a high rate of deviation. Others presented, through SEM images, that the fiber diameters of wool materials range between 2 and 8 um inside the material [41]. [PERSON] et al. [27] presented a visualization of glass micro-fibers with scanning electron microscopy. From our group of SEM images (Figure 2a-d), it can be seen the sample's fiber is cylindrical, and the fibers are embedded in a binder material; moreover, the orientation of the fibers is randomized. For analysis of the samples, 700\(\times\) magnification with a 2 kV acceleration voltage was used to minimize charge accumulation effects. By comparing the SEM images of the studied samples, the most important difference in morphology was that in the case of the yellow sample, the surface of the fibers was covered with a higher density of small particles. At the same time, the composition measurements did not show any difference between the samples. From the EDS results, we could deduce that the base components were almost the same for the four materials: carbon, silicon, aluminum, sodium, magnesium, and calcium, possibly with their oxides; moreover, a slight sign of sulfur was sometimes observed. However, significant sulfur was not detected in the green sample. By analyzing the fiber's diameter with the ImageJ software, we could state that the distribution of the fibers also varied significantly within the material, and the inhomogeneity of the fibers was high. The scanning electron microscopy images show a typical image of fibrous insulation materials. The material was lightweight and compressible. It was also notable that the surface of the fibers was smooth only for the green samples. Gaussian distribution analysis was also performed on the measured fiber diameters, and the results are shown in Figure 2a-d as well as in Table 2. The model used a built-in module of the data analyzing software. The equation of the model is given as Equation (2). \[\mathrm{y}=\mathrm{y}\mathrm{o}+(\mathrm{A}/(\mathrm{w}\cdot\mathrm{sqrt} \mathrm{(Pi/2))})^{*}\mathrm{exp}(-2((\mathrm{x}\cdot\mathrm{xc})/\mathrm{w}) \cdot 2 \tag{2}\]  
where \(\mathrm{w}\) is the width, \(\mathrm{A}\) is the area, \(\mathrm{y}\mathrm{o}=0\) is the offset, \(\mathrm{xc}=\mathrm{is}\) the center, and sigma is half of the width as the standard deviation. The values of these parameters are shown in Table 2. From the adjusted R-squared, it could be concluded that the model fitted very well to the data (fiber diameters) of the pink samples, and acceptably for the yellow and green samples. Moreover, the mean values of the measured data of the fiber diameters have also been calculated. The main parameters of Gaussian distribution analysis are the center (xc) and sigma. It could be seen that the value of the center was the highest for the green sample. Smaller fiber diameters were detected for the other samples. At the same time, from the results of the histograms in Figure 2a-d, it was found that the largest inhomogeneity in fiber diameter was identified out for the green, yellow, and orange samples. For these materials, fiber diameters varied between 0.5 and 6.6 mm. To observe the relationship between the as-received density and the calculated mean fiber diameter, Figure 3 was created. This graph presents the fiber diameters of each sample. From the Gaussian distribution of the above figures, it could be determined that the mean value versus standard deviation ratio was 0.3 for pink, 0.41 for yellow and green, and 0.46 for the orange samples.