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The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data, demandingthe deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data needs to be managed, preserved, and shared. Its heterogeneity calls for highly flexible data models to represent data from fabrication workflows, measurements, and simulations. We propose employing a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we have created an EMMO-based ontology, where EMMO stands for European Materials Modelling Ontology. The Python framework Django is used to allow for the intuitive integration into the virtual materials intelligence platform, VIMI. The Django framework relies on the Object-Graph-Mapper (OGM) neomodel to create a mapping between database classes and python objects. The model can store the whole bandwidth of data, from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research.

Maintaining a well-balanced water distribution is crucial for the operation of polymer electrolyte fuel cells. The water distribution depends to a large extent on the ease of liquid water transport in the diffusion media (DM) and in the flow field (FF) on the cathode side and, especially, on processes in boundary regions between these media. In this article, the droplet dynamics at the DM-FF interface are studied by a mechano-hydrodynamic pore model. The presented treatment allows droplet detachment to be rationalized, in dependence of pore radius and air flow velocity. Large pore radius and high air flow velocity favor droplet detachment during an earlier stage of growth, at which the contact line of the droplet remains pinned at the pore opening while the droplet volume expands during growth. We evaluate the trend of detachment time and detachment height of a droplet. Furthermore, we analyze the influence of the air flow velocity on the time-averaged liquid pressure at the pore exit. The physics governing the flooding behavior of the pore is discussed. The presented model-based analyses provide theoretical insights into material properties and operating conditions that improve water removal.

Understanding the local reaction conditions at the catalyst-ionomer interfaces inside of polymer electrolyte fuel cells is vital for improving cell performance and stability. Properties of the water film and distributions of protons and oxygen molecules at the catalyst-ionomer interface are affected by the state of the catalyst and support surfaces and the structure of the ionomer skin layer. In this work, the interfacial region between catalyst and support surface and ionomer skin is simulated using molecular dynamics. This water-filled nanopore model is constructed to study the impact of local charge density, density of sidechains at the ionomer layer, and water layer thickness on the water structure and electrostatic conditions in the pore as well as the transport properties of water, hydronium, and molecular oxygen at the interface. The analysis of the flooded pore model indicates that surface hydrophilicity, represented by water adsorption and the formation of an ordered water layer at the surface, is a major factor determining the interfacial proton density, ionomer sidechain mobility, and interfacial oxygen transport resistance. The results obtained can guide the design of new catalyst materials, where the hydrophilicity of the surface can be tailored to minimize the local proton transport resistance and improve electrode performance.

The performance of polymer electrolyte fuel cells decisively depends on the structure and processes in membrane electrode assemblies and their components, particularly the catalyst layers. The structural building blocks of catalyst layers are formed during the processing and application of catalyst inks. Accelerating the structural characterization at the ink stage is thus crucial to expedite further advances in catalyst layer design and fabrication. In this context, deep learning algorithms based on deep convolutional neural networks (ConvNets) can automate the processing of the complex and multi-scale structural features of ink imaging data. This article presents the first application of ConvNets for the high throughput screening of transmission electron microscopy images at the ink stage. Results indicate the importance of model pre-training and data augmentation that works on multiple scales in training robust and accurate classification pipelines.

Electrochemical pressure impedance spectroscopy (EPIS) analyses the voltage response of a polymer electrolyte fuel cell (PEFC) as a function of an applied pressure signal in the frequency domain. EPIS is similar to electrochemical impedance spectroscopy (EIS) and its development was inspired by the diagnostic capabilities of the latter. The EPIS introduced in this work modulates the cathode pressure of a PEFC with a sinusoidal signal through the use of a back-pressure controller, and monitors the cell voltage while holding the cell at a constant current. A sinusoidal pressure wave propagates along the flow field channels because of this pressure modulation. This pressure wave impacts local reaction rates and transport properties in the cathode, resulting in a sinusoidal voltage response. The amplitude ratio and phase difference between these two sinusoidal waves entail diagnostic information on processes that take place within the PEFC. To demonstrate the utility of the EPIS technique, experiments have been carried out to measure and analyze the frequency response of PEFCs with two different flow fields. A parametric study has been conducted to characterize the effect of pressure oscillation amplitude, load, oxygen concentration, oxygen stoichiometry and cathode gas flow rate on the EPIS signal.

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