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Abstract We report a large-angle rocking beam electron diffraction (LARBED) technique for electron diffraction analysis. Diffraction patterns are recorded in a scanning transmission electron microscope (STEM) using a direct electron detector with large dynamical range and fast readout. We use a nanobeam for diffraction and perform the beam double rocking by synchronizing the detector with the STEM scan coils for the recording. Using this approach, large-angle convergent beam electron diffraction (LACBED) patterns of different reflections are obtained simultaneously. By using a nanobeam, instead of a focused beam, the LARBED technique can be applied to beam-sensitive crystals as well as crystals with large unit cells. This paper describes the implementation of LARBED and evaluates the performance using silicon and gadolinium gallium garnet crystals as test samples. We demonstrate that our method provides an effective and robust way for recording LARBED patterns and paves the way for quantitative electron diffraction of large unit cell and beam-sensitive crystals. 

We investigate intermittent plasticity in nanopillars of nanocrystalline molybdenum based on in situ transmission electron microscopy observations. By correlating electron imaging results with the measured nanopillar mechanical response, we demonstrate that the intermittent plasticity in nanocrystalline molybdenum is largely caused by dislocation avalanches. Electron imaging further reveals three types of dislocation avalanches, from intragranular to transgranular to cross-granular avalanches. The measured strain bursts resulted from avalanches have similar magnitudes to those reported for the molybdenum single-crystal pillars, while the corresponding flow stress in nanocrystalline molybdenum is greatly enhanced by the small grain size. Statistical analysis also shows that the avalanches behavior has similar characteristic as single crystals in the mean field theory model. Together, our findings here provide critical insights into the deformation mechanisms in a nanostructured body-centered-cubic metal. 

Here, we introduce a novel defect imaging method based on the cepstral analysis of electron diffuse scattering using an Electron Microscope Pixel Array Detector (EMPAD) detector. 

This talk focuses on the principles of 4D-STEM based electron nanodiffraction techniques for defect, strain and short-range ordering analysis using electron diffuse scattering [8, 9]. We review recent progress made in scanning electron nanodiffraction (SEND) data collection, new algorithms based on cepstral analysis, and machine learning based electron DP analysis. These progresses will be highlighted using defect detection, and short-range ordering analysis as application examples. The materials of the study are the medium entropy alloy, CrCoNi, which has exceptional low-temperature mechanical strength and ductility. We will show how SEND helps our understanding of non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering, behind the mechanical strength in CrCoNi and how these developments provide general opportunities for an atomistic-structure study in advanced alloys. 

Compared with the Fe40Mn20Cr20Ni20 high-entropy alloy in an homogenized state, it has higher incipient plastic strength after high-temperature aging, which is attributed to the generation of short-range orderings (SROs) caused by the local composition fluctuations. Based on nanoindentation results at different loading rates, the evolution trends of homogeneous and heterogeneous dislocation-nucleation modes under the effect of SROs are revealed for the first time. Under the action of the high-solution friction stress, which is caused by the high loading rate, and coherency-strain field, which is caused by SROs, the critical shear stress of the dislocation nucleation increases. Furthermore, with the increase of the loading rate, the probability of heterogeneous nucleation in homogenized samples increases, while that in aged samples is the opposite. From the perspective of the distribution of dislocation-nucleation sites, this opposite trend can be well explained by assuming the spreading resistance of an activatable region. In short, the present work reveals the pivotal role of SROs on dislocation-nucleation modes and paves the way for the quantitative study concerning SROs and their strengthening effects. 

In the pursuit of developing high‐temperature alloys with improved properties for meeting the performance requirements of next‐generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature‐dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature,T_break, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black‐box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low‐ and high‐temperature regimes, is presented. The results presented extend previous work on high‐entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature‐dependent strength behavior of superalloys as well as HEAs.