Deep learning based segmentation of binder and fibers in gas diffusion layers

Abstract

Gas diffusion layers (GDLs) are vital parts for the performance of proton-exchange membrane fuel cells (PEMFCs). In many cases, they are made of Carbon-Carbon Composite Paper (CCCP), which consists of carbon fibers and a carbonized binder material. The distribution of the fibers and binder in the GDL strongly influences the performance of a PEMFC. Synchrotron scans are a great way to obtain information about the microstructural composition of carbon paper GDLs (Figure 2), but there is one major obstacle. Binder and fibers tend to have the same attenuation and, consequently, the same gray values in the scans. To overcome this, we introduce a machine learning-based method that segments fibers and binder from the local morphology of a CCCP. The training data is generated using FiberGeo, a module in the GeoDict software for fibrous microstructure generation. FiberGeo creates fibers based on stochastic geometry and adds binder using morphological opening and closing operations. We applied the machine learning-based method to four Scans of samples of Toray Carbon Paper with varying amounts of binder in them. The result is the quantification of individual voxels as fiber or binder material that can be used, for example, in performance simulations of property simulations in PEMFCs [1–4]. Here, we focus on the differences in the spatial distribution of the binder both in the through-plane and in-plane directions.