Regarding computational modelling, it has played a significant role in advancing our understanding of the complex phenomena taking place in PEMFCs. One approach to digitally generate GDLs microstructures involves the utilization of stochastic reconstruction methods. Stochastic reconstruction methods encompass computational procedures employed to generate random or probabilistic depictions of intricate structures or systems, typically grounded in statistical principles or random processes. These techniques find widespread application across diverse domains such as materials science, image processing, and computer graphics, facilitating the simulation and analysis of complex phenomena. Stochastic reconstruction methods enable the exploration of a wide range of GDL designs, facilitating the identification of optimal microstructural configurations that enhance the performance of GDL, and ultimately, fuel cell. Regarding the study of effective transport properties, the Lattice Boltzmann Method (LBM) has been proven as an outstanding approach. For example, Sarkezi-Selsky et al. calculated the effect of PTFE on water transport through the GDL, concluding that increasing the amount of the hydrophobic binder led to lower water saturation levels for a given capillary pressure, suggesting that increasing the hydrophobicity of the GDL reduces the risk of flooding. In addition, Wu et al. employed LBM to calculate the effective transport physical properties as well as the effective thermal and electrical conductivities, diffusivity of reconstructed GDL/CL and permeability of reconstructed GDL. Tayarani-Yoosefabadi et al. developed a multiscale modelling tool for MPLs and MPL-coated GDLs considering various materials.