## 2 Results and Discussions
### Comparative Analysis Between Different Techniques  
A linearity study was performed using the PE values obtained from each technique, confirming their correlation. The analysis of DIP versus SPC yielded a correlation coefficient of R\({}^{2}\) = 0.952 (Figure 6a), demonstrating a robust linear relationship and validating both DIP and SPC as effective methods for assessing substrate self-cleaning. Figure 6: \(Cont.\)  
The correlation between UV-Vis and SPC (R\({}^{2}\) = 0.617) (Figure 6b), as well as DIP (R\({}^{2}\) = 0.648) (Figure 6c), was moderate, probably due to variations in experimental parameters such as concentration and irradiation. All the models showed statistical significance (\(p\) < 0.05). A direct comparison of the evaluation techniques is challenging due to the technical specificities of each method, including differences in the dye concentrations used as the basis for degradation, the irradiation conditions, and performance measurement. However, all approaches showed a common trend: the functionalized samples consistently exhibited a higher PE than the control samples, demonstrating that the nano-TiO\({}_{2}\) coating effectively enhanced the photocatalytic degradation potential of the material. Both SPC and DIP have proven to be a practical and efficient approach to evaluate photocatalytic efficiency, which explains their widespread use. In addition to evaluating photocatalysis, both techniques allow for the visual monitoring of esthetics, helping to preserve the original appearance of the substrate, an essential feature for applications in urban facades and exterior coatings. However, these colorimetric techniques have specific characteristics, such as the difficulty in detecting subtle variations at very low dye concentrations, which does not enable the use of concentrations similar to those used in UV-Vis. In addition, the characteristics of the substrate can interfere with the trends, complicating the distinction between the manipulation of the dye and the natural color of the material. By its nature, colorimetry only evaluates visible color changes and does not provide complete information on the behavior of substances that do not cause visual changes so applicability may be limited in real-world contexts. The color of the substrate surface is a significant limitation for techniques that rely on surface analysis, such as SPC and DIP. Dark surfaces absorb more light and reflect less, making it difficult to detect changes in dye degradation and reducing the accuracy of these methods. For very dark samples such as, for example, asphalt or darker cementitious concrete surfaces, these limitations make surface-based analyses impractical. Although SPC is relatively simple, it requires specialized and expensive equipment for conducting measurements. In contrast, DIP proved to be even faster and more straightforward, providing consistent and reliable results compared to SPC. Its pixel-by-pixel analysis enabled the mapping of changes across the entire sample surface, increasing precision and resulting in slightly better measurements. In addition, DIP proved to be an affordable alternative, eliminating the need for expensive laboratory equipment. However, its reliability depended on carefully controlled lighting conditions to ensure the reproducibility of results. Table 1 provides a summary of the main aspects of each technique. Figure 6: Scatter plot showing the relationship between photocatalytic efficiency across different methods: (**a**) DIP vs. SPC, (**b**) UV-Vis vs. SPC, and (**c**) UV-Vis vs. DIP. The black squares represent individual data points, while the red line indicates the best-fit trend line. UV-Vis is a versatile technique that enables the monitoring of a wide range of pollutants, including those which do not exhibit visible color. With high sensitivity to small variations in concentration, it allows the detection of minimal changes over time. However, its application to cementitious substrates, which exhibit a low adsorption capacity, requires careful monitoring.