For the first time, machine learning methods are applied to the gas diffusion layer (GDL) to
characterize structural and transport properties. In the first investigation, a benchmark study …

Three dimensional convolutional neural networks (3D CNNs) were used to accurately
predict the permeability of fuel cell gas diffusion layer (GDL) materials directly from 3D …

In this research, we introduce a novel approach that employs a 3D convolutional neural
network (CNN) model to predict the permeability of Gas Diffusion Layers (GDLs). For training …

The gas diffusion layer (GDL) is a key component in a proton exchange membrane fuel cell
and a comprehensive understanding of its transport properties is imperative for improving …

Abstract Machine learning algorithms trained on data gathered from stochastically
generated gas diffusion layers (GDLs) were used to predict key transport properties that …

Gas diffusion layers (GDLs) play an important role in proton exchange membrane fuel cells
(PEMFCs) for the diffusion of reactant and the removal of product water. In the current study …

Obtaining transient flow field information of gas diffusion layers (GDLs) is a crucial issue for
improving the performance of proton exchange membrane fuel cells (PEMFCs). While …

Nano-computed tomography (nanoCT) was used for non-invasive 3D visualization and
characterization of porous gas diffusion layer (GDL) for polymer electrolyte membrane fuel …

The material characteristics of gas diffusion layers are relevant for the efficient operation of
polymer electrolyte fuel cells. The current state-of-the-art calculates these using transport …

In X-ray computed tomography (CT) reconstructions of gas diffusion layers (GDLs),
grayscale threshold selection is a critical issue. Although various selection methods exist …