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Development of nuclear grade, iron-based wrought FeCrAl alloys has been initiated for light water reactor (LWR) fuel cladding to serve as a substitute for zirconium-based alloys with enhanced accident tolerance. Ferritic alloys with sufficient chromium and aluminum additions can exhibit significantly improved oxidation kinetics in high-temperature steam environments when compared to zirconium-based alloys. In the first phase, a set of model FeCrAl alloys containing 10-20Cr, 3-5Al, and 0-0.12Y in weight percent, were prepared by conventional arc-melting and hot-working processes to explore the effect of composition on the properties of FeCrAlY alloys. It was found that the tensile properties were insensitive to the alloy compositions studied; however, the steam oxidation resistance strongly depended on both the chromium and the aluminum contents. The second phase development focused on strengthening Fe-13Cr-5Al with minor alloying additions of molybdenum, niobium, and silicon. Combined with an

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Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison-based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

Concrete, used in the construction of nuclear power plants (NPPs), may be exposed to radiation emanating from the reactor core. Until recently, concrete has been assumed immune to radiation exposure. Direct evidence acquired on Ar+-ion irradiated calcite and quartz indicates, on the contrary, that, such minerals, which constitute aggregates in concrete, may be significantly altered by irradiation. More specifically, while quartz undergoes disordering of its atomic structure resulting in a near complete lack of periodicity, calcite only experiences random rotations, and distortions of its carbonate groups. As a result, irradiated quartz shows a reduction in density of around 15%, and an increase in chemical reactivity, described by its dissolution rate, similar to a glassy silica. Calcite however, shows little change in dissolution rate - although its density noted to reduce by ≈9%. These differences are correlated with the nature of bonds in these minerals, i.e., being dominantly ionic or covalent, and the rigidity of the mineral’s atomic network that is characterized by the number of topological constraints (nc) that are imposed on the atoms in the network. The outcomes have major implications on the durability of concrete structural elements formed with calcite or quartz bearing aggregates in nuclear power plants.