# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 3 Results and Discussion
### Self-Cleaning Assessment by CIELAB Color Space  
The photocatalytic efficiency calculated through spectrophotometric (PEs) and AI-enhanced (PEai) colorimetry techniques is presented in Figure 5. The results show that pollutant degradation (self-cleaning) increased with irradiation time, and higher amounts of nano-TiO\({}_{2}\) in the paint led to increased photocatalytic efficiency. Thus, samples with 3% nano-TiO\({}_{2}\) achieved PEs above 70% after 48 h, while the reference sample reached just over 20%. Figure 3: Visual analysis of sample discoloration over irradiation periods. Figure 4: (a) L\({}^{*}\); (b) a\({}^{*}\); and (c) b\({}^{*}\) values obtained from spectrophotometric versus AI-enhanced colorimetry methods. PEai confirmed this trend, showing that higher concentrations of nano-TiO\({}_{2}\) provided a greater self-cleaning capability to the paints. For example, the sample with 3% had the highest photocatalytic efficiency (above 80%) compared to the 34% of the reference paint. The linearity between PEs and PEai is shown in Figure 6, demonstrating a very high determination coefficient (R\({}^{2}\) = 0.99018). This confirms that AI-enhanced colorimetry is an effective alternative for measuring self-cleaning on substrates. This linearity was expected, as color coordinates a* and b*, which were used to calculate photocatalytic efficiency, presented determination coefficients of 0.98717 and 0.92296, respectively. The second colorimetric parameter used to evaluate self-cleaning was the \(\Delta\)E. It considers the variation in the three CIELAB color coordinates (Figure 7) and increases due to greater pollutant degradation on the sample surface (self-cleaning). Furthermore, the results concerning the colorimetric parameters (CS, PE, and \(\Delta\)E) for self-cleaning assessment are properly identified in the Supplementary Materials in Tables S3 and S4. Figure 5: Photocatalytic efficiency calculated through (**a**) spectrophotometric (PEs) and (**b**) AI-enhanced (PEai) colorimetry. Figure 6: PEs versus PEai. Figure 7: Color variation calculated through (**a**) spectrophotometric (\(\Delta\)Es) and (**b**) AI-enhanced (\(\Delta\)Eai) colorimetry. The \(\Delta\)Es showed that color change increased with irradiation time and was directly proportional to the concentration of the semiconductors. Thus, the sample with 3% nano-TiO\({}_{2}\) showed the highest color variation, reaching approximately 30 after 48 h of irradiation due to its higher photocatalytic efficiency. The \(\Delta\)Eai also confirmed that the increase in color change was more pronounced for samples with higher amounts of incorporated nanomaterial. In this case, the sample with 3% nano-TiO\({}_{2}\) revealed the highest color variation, exceeding 35 units after 48 h of irradiation. As expected, the correlation between \(\Delta\)Es and \(\Delta\)Eai is high (Figure 8), with an R\({}^{2}\) of 0.97825. However, similarly to the PEai values, which were relatively higher than the PEs, the \(\Delta\)Eai values were also higher than the \(\Delta\)Es. Several factors might have led to the higher values of PEai and \(\Delta\)Eai, such as the lower correlation of the L* coordinate between both techniques, the illumination projected by the photobox on the samples, the area of the sample analyzed, the non-uniform pollutant dispersion on the sample, and the equipment calibration and configuration, among others. However, this comparison was essential to highlight that the AI-enhanced colorimetry method is a promising and highly efficient tool, enabling the measurement of color variations and photocatalytic efficiency, among other parameters. The use of AI eliminates the need for expensive and sophisticated equipment, such as a spectrophotometer, making the process more accessible and cost-effective. Additionally, AI offers the advantage of quickly analyzing large areas and volumes of data with high precision and consistency, ensuring reliable results.