Bridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence

X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials' absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.Comment: 33 pages, 5 figure

Composite Cathode for High-Power Density Solid Oxide Fuel Cells

Reduction of solid oxide fuel cell (SOFC) operating temperature will play a key role in reducing the stack cost by allowing the use of low-cost metallic interconnects and new approaches to sealing, while making applications such as transportation more feasible. Reported results for anode-supported SOFCs show that cathode polarization resistance is the primary barrier to achieving high power densities at operating temperatures of 700 C and lower. This project aims to identify and develop composite cathodes that could reduce SOFC operating temperatures below 700 C. This effort focuses on study and use of (La,Sr)(Co,Fe)O{sub 3} (LSCF) based composite cathodes, which have arguably the best potential to substantially improve on the currently-used, (La,Sr)MnO{sub 3}-Yttria-stabilized Zirconia. During this Phase I, it was successfully demonstrated that high performances can be achieved with LSCF/Gadolinium-Doped Ceria composite cathodes on Ni-based anode supported cells operating at 700 C or lower. We studied electrochemical reactions at LSCF/Yttria-stabilized Zirconia (YSZ) interfaces, and observed chemical reactions between LSCF and YSZ. By using ceria electrolytes or YSZ electrolytes with ceria diffusion barrier layers, the chemical reactions between LSCF and electrolytes were prevented under cathode firing conditions necessary for the optimal adhesion of the cathodes. The protection provided by ceria layer is expected to be adequate for stable long-term cathode performances, but more testing is needed to verify this. Using ceria-based barrier layers, high performance Ni-YSZ anode supported cells have been demonstrated with maximum power densities of 0.8W/cm2 at 700 C and 1.6W/cm{sup 2} at 800 C. Ni-SDC anode supported cells with SDC electrolytes yielded >1W/cm{sup 2} at 600 C. We speculate that the power output of Ni-YSZ anode supported cell at 700 C and lower, was limited by the quality of the Ceria and Ceria YSZ interface. Improvements in the low-temperature performances are expected based on further development of barrier layer fabrication processes and optimization of cathode microstructure