The US navy has a critical need for air independent advanced electric power sources to replace batteries in unmanned undersea vehicles (UUVs). Solid oxide fuel cells (SOFCs) are being considered as one potential replacement option. However, SOFCs typically operate using atmospheric air as their oxidant which is not an option for this underwater application. For this application, pure pressurized oxygen would be used as the oxidant which motivates the search for a cathode material which would be optimal for a high oxygen partial pressure environments. Specifically, this research focuses on cathode materials which can exploit the unique operating conditions required for UUVs. The operation in 100% oxygen atmosphere rather than air provides a significant opportunity. This is because oxygen surface exchange and bulk transport through the cathode is mediated through point defects whose concentrations are sensitive to the partial pressure of oxygen in the atmosphere surrounding the cathode. Oxygen bulk transport along with oxygen surface exchange are the rate controlling steps in oxygen reduction and incorporation at the cathode. The focus of this research is to examine the relationship between oxygen partial pressure and its effect on SOFC cathode performance for two different families of cathode materials, namely strontium doped lanthanum manganite, and a relatively new class of cathode materials, rare-earth nickelates. The experimentally measured relationship between cathode polarization and oxygen partial pressure will be correlated with the underlying transport and surface exchange processes in both families of materials.

<p> Surface probes are producing a huge variety of spatially resolved images of materials during phase transitions. These images have complex pattern formation present across a variety of length scales. Here, I apply image cluster scaling analysis and machine learning to several such images. First, I apply cluster analysis techniques to charge stripe orientations in Bi2−zPbzSr2−yLayCuO6+x. Our experimental collaborators observe stripes with period 4a0 in Bi2−zPbzSr2−yLayCuO6+x. [1] The local orientation of these stripes forms complex patterns from which we extract relationships involving cluster sizes. We compare these experimental exponents to those computed at a phase transition in the following models: 2D percolation and the 2D and 3D clean and random field Ising models. We find that only the 3D clean and random field Ising models are consistent with the data. Combined with the stability of these exponents across the superconducting region, we conclude that the system is in the random field Ising model universality class. We apply these same cluster techniques to period-4 antiferromagnet order in NdNiO3. [2] Our experimental collaborators observed the intensity for 2 of 8 possible directions for period-4 antiferromagnetic order in NdNiO3 and find complex pattern formation that remains after a temperature cycle past the hysteresis loop. We threshold this experimental data and extract cluster exponents for this system. We then compare these models to the 4-state clean and random field clock models. This exponent comparison shows that the 4-state random field clock model is a match for the experimental data. We then train a convolutional neural network to distinguish the 4-state clean and random field clock models. The fit neural net is capable of labeling our entire testing dataset of 16000 images with 100% accuracy. This gives us a 95% confidence interval of (0.9998, 1) by the rule of three. [3] We then split the field of view into 52 sliding windows of the original experimental data which we feed into the trained model. The model classifies every input window as a 2D random field clock model which gives us a 95% confidence interval of (0.94, 1). The observed hysteresis in the experimental data, the cluster analysis and the machine learning prediction clearly show the observed patterns are in the random field 4-state clock model universality class. </p>