Nanocrystalline materials have been proposed as superior radiation tolerant materials in comparison to coarse grain counterparts. However, there is still a limited understanding whether a particular nanocrystalline grain size is required to obtain significant improvements in key deleterious effects resulting from energetic irradiation. This work employs the use of in-situ heavy ion irradiation transmission electron microscopy experiments coupled with quantitative defect characterization and precession electron diffraction to explore the sensitivity of defect size and density within the nanocrystalline regime in platinum. Under the explored experimental conditions, no significant change in either the defect size or density between grain sizes of 20 and 100 nm was observed. Furthermore, the in-situ transmission electron microscopy irradiations illustrate stable sessile defect clusters of 1-3 nm adjacent to most grain boundaries, which are traditionally treated as strong defect sinks. The stability of these sessile defects observed in-situ in small, 20-40 nm, grains is the proposed primary mechanism for a lack of defect density trends. This scaling breakdown in radiation improvement with decreasing grain size has practical importance on nanoscale grain boundary engineering approaches for proposed radiation tolerant alloys.
Prior studies on the high-cycle fatigue behavior of nanocrystalline metals have shown that fatigue fracture is associated with abnormal grain growth (AGG). However, those previous studies have been unable to determine if AGG precedes fatigue crack initiation, or vice-versa. The present study shows that AGG indeed occurs prior to crack formation in nanocrystalline Ni-Fe by using a recently developed synchrotron X-ray diffraction modality that has been adapted for in-situ analysis. The technique allows fatigue tests to be interrupted at the initial signs of the AGG process, and subsequent microscopy reveals the precursor damage state preceding crack initiation.
The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.