2.1.1. Technical Materials

For this study, we used a Solar Africa polycrystalline solar panel with the following electrical characteristics:

1) Maximum power (Pm): 35 W;

2) Maximum voltage (Vmp): 17.6 V;

3) Maximum current (Imp): 2 A;

4) Open-circuit voltage (Voc): 21 V;

5) Short-circuit current (Isc): 2.24 A.

This panel was recovered from the Ancienkan neighborhood, located in the PALMCI village, near Divo, a city in the center-west of Côte d’Ivoire. Figure 1 below shows the polycrystalline solar panel studied in our study.

Figure 1. Photograph of the polycrystalline solar.

2.1.2. Experimental Device

Figure 2 shows the sieve used to sift the glass crystals and the VWR brand electronic balance shown in Figure 3 allowed us to make the different weighing of each quantity of powder of the panel components.

Figure 2.Sieve.

Figure 3.Electronic balance.

The glass crystal and filament samples were ground using a Retsch 200 mill (Figure 4) in manual mode, with a speed of 1100 rpm for 2 minutes and 40 seconds. These parameters were optimized to obtain a homogeneous powder while limiting mechanical heating and contamination, thus preserving the structural and chemical integrity of the materials.

Figure 4. Ritsch 200 Mill.

Image acquisition was performed using a smartphone equipped with a 26 mm wide-angle lens with an f/1.6 aperture. The 12-megapixel sensor with 1.4 µm pixels provides high-quality images, and autofocus enables sharp digital images.

MATLAB R2022a software, released on March 9, 2022, was used for data processing and visualization.

2.2.1. Solar Panel Disassembly

To disassemble the solar panel, a hammer was first used to gently break the glass into small fragments, facilitating its removal. The filaments were then manually extracted and cut into smaller sections using chisels for grinding [5].

2.2.2. Preparation of the PV Glass and Filament Powder

15 g of glass and filament were collected for powder processing. The glass and filament were ground separately using an electric grinder, reducing each material to fine, homogeneous particles. Before grinding, the resulting particles were sieved through a screen to separate the different particle sizes. Figure 5 shows the glass and filament powders of the solar panel.

Figure 5. Glass crystals (A); Crushed glass powder (B); PV filament (C); Crushed filament powder (D).

2.2.3. Gabor Filter

In the analysis of the textures of glass and filament powders, the application of the Gabor filter made it possible to extract local characteristics related to frequency and orientation [6]. For this, we used a wavelength fixed at 4, which allows capturing fine details, as well as a maximum orientation of 90˚, corresponding to the detection of vertically oriented patterns in the image [7]. This approach allowed us to obtain, for each material (glass and filament), two images resulting from the transformation: the magnitude image, which highlights the areas with a strong textural response, and the phase image, which provides information on the fine structure and organization of the patterns [8].

If we have an image I ( x , y ) of dimension M × N pixels, its discrete Gabor wavelet transform is obtained by convolution with m orientations and n frequencies [9],

where G m n ( x , y ) is the Gabor wavelet transform, s, t are summation variables ψ * is the conjugate of Ψ ( x , y ) such that:

Where Ψ ( x , y ) represents the 2D Gabor function σ x and σ y are the standard deviations along the x and y axes, respectively ƒ is the spatial frequency of the Gabor filter.

The analysis of the control powder table (Ag, Cu, Si, CaO, Na₂O, SiO₂) was performed using the Gabor filter applied to the obtained images (Figure 6(A) and Figure 6(B)). This approach made it possible to extract textural characteristics at two levels: magnitude, which provides information on the intensity of the detected structures, and phase, which highlights the orientation and continuity of the patterns. The use of these two components makes it possible to identify, for each material, a textural characteristic, useful for differentiating and identifying constituents in complex mixtures.

Figure 6. Filament witness powders (A); Glass witness powders (B).

3.2.1. Segmentation of the Magnitudes of the Different Witness Powders and the PV

The segmentation was performed on the magnitude, considering areas with comparable textural intensity levels. For each segmented region, a set of parameters was extracted (at, bt, ct, dt, E, S) [11]. The results of this segmentation are presented in Figure 7.

Figure 7. Segmentation of the magnitudes of the control powders (A) and (B); Segmentation of the magnitudes of the glass powders (C); Segmentation of the magnitudes of filament powders (D).

3.2.2. Segmentation of the Phases of the Different Witness Powders and the PV

Following the magnitude-based analysis, regional segmentation was also applied to the images from the Gabor filter phase, in order to better characterize the spatial and directional organization of the structures present in the control and photovoltaic powders [12]. Figure 8 illustrates the segmentations performed on the control powders and the PV glass and filament powders.

Figure 8. Phase segmentation of the filament (A) and glass (B) powders, Phase segmentation of the filament (C) and glass (D) powders.

3.3.1. Characteristic Values Extracted from Magnitudes and Phases

Tables 1-3 and Table 4 group, respectively, the parameter values (at, bt, ct, dt, E, and S) of the magnitudes of the control glass and filament, the parameter values of the magnitudes of the PV glass and filament, the parameter values of the phases of the control glass and filament, and finally the parameter values of the phases of the PV glass and filament.

Table 1. Values of magnitude parameters.

Table 2. Parameters of the magnitudes of the PV powders.

Table 3. Phase parameter data of control samples.

Table 4. PV phase parameter data.

These different values made it possible to construct histograms in Figure 9 which give the distribution of parameter values according to the components (CaO, Na₂O, SiO₂, Cu, etc.) of the solar panel.

Figure 9. Magnitude and phase histograms of witnesses and PV (Filament and Glass).

The phase and magnitude histograms show, for each region analyzed (RV1, RV2, RV3, RF1, RF2, RF3), the distribution of textural parameters (at, bt, et, S, E, ct). By comparing the profiles of the regions from the panel (glass and filament) with those of the controls, the chemical elements present in each region can be identified [13].

Analysis of the magnitude histograms confirms the trends observed with the phase. The R’V1, R’V2, and R’V3 regions respectively show a strong presence of Na₂O, CaO, and SiO₂, while the RF1, RF2, and RF3 zones are characterized by the dominant presence of Si, Cu, and Ag. Although the ct bar is predominant, the relative distribution of the other components (at, S, E, etc.) allows for the identification of the elements present in each region and the elimination of the presence of certain elements in the segmented region.

3.3.2. Ore Identification by Euclidean Distance

We used the normalized Euclidean distance to calculate the distance between the characteristic vectors of the components (ores). It is calculated using the following formula:

Normalized Euclidean distance values were calculated to assess the magnitude variations between the control powders (glass and filament) and those from the photovoltaic panels. Table 5 and Table 6 present these distances, thus showing the similarity between the vectors of the different regions analyzed.

Table 5. Normalized Euclidean distances for the magnitude of control powders and PV powders.

Table 6. Normalized Euclidean distances for the phase of control powders and PV powders.

The distances are very small, ranging from 0.0032 to 0.0281 for magnitude, and from 0.0041 to 0.0124 for phase. These values are well below the threshold of 0.05 commonly used in the scientific literature to indicate a high similarity between two samples [14][15]. This indicates that the powders from the photovoltaic panel have a chemical composition very close to that of the control powders. The magnitude analysis highlighted that some segmented regions of the image, such as R’V1, R’V2 and R’V3, are strongly correlated with the spectral signatures of Na₂O, CaO and SiO₂. Other regions, such as RF1, RF2 and RF3, show similarity with the reference powders containing Si, Cu and Ag. For the phase, the regions RV1, RV2 and RV3 also show very close profiles of CaO, SiO₂ and Na₂O. These results show that the Gabor filter, applied to powder images, not only allows textures to be differentiated, but also allows the chemical signatures characteristic of the elements present to be identified. A comparative analysis with classical chemical methods, such as infrared spectroscopy and X-ray fluorescence, was carried out to validate these results. The studies of [16] and [17] all identified the same elements in polycrystalline photovoltaic panels as those detected in our study (Si, Ag, Cu, CaO, Na₂O, SiO₂). These comparisons reinforce the reliability of the results obtained with our image analysis method. Moreover, these results are consistent with those of [18][19], who also used techniques based on texture analysis and Euclidean distances to identify chemical elements in materials. This methodological convergence shows that our approach is robust and can be used effectively in the characterization of photovoltaic materials. However, it is important to highlight some limitations of our method. Indeed, elements such as lead and tin, identified by [20] in photovoltaic panels using conventional chemical methods (EDS and atomic absorption spectroscopy), could not be detected in our study. This is explained by the fact that these elements were not part of the control powders selected for comparison, and that image segmentation does not always allow distinguishing all the components present in a complex sample.