Porous materials play a central role in many technologies, from micron-thick electrodes used in batteries and fuel-cells (Karan, 2017) to kilometer-long geologic formations of interest in oil recovery, contaminant transport and CO2 sequestration (Kumar et al., 2005). These applications share a common interest in analyzing the transport processes occurring at the pore-scale, since these ultimately control the observable macroscopic behavior (Anovitz & Cole, 2015). Images of porous materials are a valuable source of information, since performance and structure are intimately linked through the topology and geometry of the media. A variety of techniques are available for imaging a material's internal pore structure with sub-micron resolution, including X-ray tomography (Coles et al., 1998), ptychography (Dierolf et al., 2010), and FIB-SEM sectioning (Heaney, Vicenzi, Giannuzzi, & Livi, 2001). Each of these tools can provide exquisitely detailed images, and retrieving quantitative information from these images has become a vital tool in all areas of porous media research.

Obtaining structural information from tomographic images of porous materials is a critical component of porous media research. Extracting pore networks is particularly valuable since it enables pore network modeling simulations which can be useful for a host of tasks from predicting transport properties to simulating performance of entire devices. This work reports an efficient algorithm for extracting networks using only standard image analysis techniques. The algorithm was applied to several standard porous materials ranging from sandstone to fibrous mats, and in all cases agreed very well with established or known values for pore and throat sizes, capillary pressure curves, and permeability. In the case of sandstone, the present algorithm was compared to the network obtained using the current state-of-the-art algorithm, and very good agreement was achieved. Most importantly, the network extracted from an image of fibrous media correctly predicted the anisotropic permeability tensor, demonstrating the critical ability to detect key structural features. The highly efficient algorithm allows extraction on fairly large images of ${500}^3$ voxels in just over 200 s. The ability for one algorithm to match materials as varied as sandstone with 20% porosity and fibrous media with 75% porosity is a significant advancement. The source code for this algorithm is provided.

The relationship of capillary pressure to liquid saturation for the water–air fluid pair in two different types of gas diffusion media (GDM) used in polymer electrolyte membrane fuel cell (PEMFC) electrodes is elucidated. It is experimentally demonstrated that GDM samples with and without treatment with poly(tetrafluoroethylene) (PTFE) ubiquitously display permanent capillary pressure hysteresis. Water does not imbibe spontaneously into a dry GDM, neither is it ejected spontaneously from a water-saturated GDM. Rather, positive displacement pressure is required to force both water and air into GDMs, whereas the main effect of adding PTFE is to increase the amount of work required for forcing water into the GDM, and to decrease the work required for water removal. Irrespective of PTFE content, the GDM samples tested are generally shown to behave as materials of intermediate (neutral) wettability. The US Bureau of Mines (USBM) wettability index nevertheless shows that water is the preferentially non-wetting phase in PTFE-treated GDMs and the preferentially wetting phase in untreated GDMs. Water–air capillary pressure curves are found to depend on sample thickness, clearly demonstrating that finite size effects are important. Finally, compression of the GDM is found to increase the capillary pressures for water injection and decrease the capillary pressures required for water withdrawal. These results should aid the design of GDMs with improved water management properties and the modeling of PEMFC electrodes in general.

The redox flow battery is a promising energy storage technology for managing the inherent uncertainty of renewable energy sources. At present, however, they are too expensive and thus economically unattractive. Optimizing flow batteries is thus an active area of research, with the aim of reducing cost by maximizing performance. This work addresses microstructural electrode optimizations by providing a modeling framework based on pore-networks to study the multiphysics involved in a flow battery, with a specific focus on pore-scale structure and its impact on transport processes. The proposed pore network approach was extremely cheap in computation cost (compared to direct numerical simulation) and therefore was used for parametric sweeps to search for optimum electrode structures in a reasonable time. It was found that that increasing porosity generally helps performance by increasing the permeability and flow rate at a given pressure drop, despite reducing reactive surface area per unit volume. As a more nuanced structural study, it was found that aligning fibers in the direction of flow helps performance by increasing permeability but showed diminishing returns beyond slight alignment. The proposed model was demonstrated in the context of a hydrogen bromine flow battery but could be applied to any system of interest.

Engineering the wettability and microstructure of gas diffusion layers offers a powerful strategy to optimize water management in polymer electrolyte fuel cells. To this goal, we recently developed a radiation grafting technique to synthesize GDLs with patterned wettability. Despite the promise of this approach, current designs are empirically-driven which hampers the development of advanced wettability patterns. To design materials with improved transport characteristics over a range of water saturations, physically representative models can be employed for the bottom-up design of gas diffusion layers with local variations in hydrophilicity. In this paper, pore network models using topology and size information extracted from high resolution tomographic images of three common gas diffusion layer materials are presented with patterned wettability. We study the influence of the substrate microstructure, the hydrophobic coating load, and the hydrophilic pattern width. It is shown that tuning the wettability leads to enhanced phase separation and increased diffusive transport which is attributed to decreased gas phase tortuosity. The network model elaborates on previous experimental studies, shedding light on the effectiveness of the radiation pattern transference and the importance of matching the masking pattern with the substrate microstructure. The work opens the door for exploration of advanced patterns, coupled with flow from gas flow field designs.