Data-driven electron-diffraction approach reveals local short-range ordering in CrCoNi with ordering effects.

The exceptional mechanical strength of medium/high-entropy alloys has been attributed to hardening in random solid solutions. Here, we evidence non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering. A data-mining approach of electron nanodiffraction enabled the study, which is assisted by neutron scattering, atom probe tomography, and diffraction simulation using first-principles theory models. Two samples, one homogenized and one heat-treated, are observed. In both samples, results reveal two types of short-range-order inside nanoclusters that minimize the Cr-Cr nearest neighbors (L12) or segregate Cr on alternating close-packed planes (L11). The L11 is predominant in the homogenized sample, while the L12 formation is promoted by heat-treatment, with the latter being accompanied by a dramatic change in dislocation-slip behavior. These findings uncover short-range order and the resulted chemical heterogeneities behind the mechanical strength in CrCoNi, providing general opportunities for atomistic-structure study in concentrated alloys for the design of strong and ductile materials.

Data-driven electron microscopy: electron diffraction imaging of materials structural properties.

Transmission electron diffraction is a powerful and versatile structural probe for the characterization of a broad range of materials, from nanocrystalline thin films to single crystals. With recent developments in fast electron detectors and efficient computer algorithms, it now becomes possible to collect unprecedently large datasets of diffraction patterns (DPs) and process DPs to extract crystallographic information to form images or tomograms based on crystal structural properties, giving rise to data-driven electron microscopy. Critical to this kind of imaging is the type of crystallographic information being collected, which can be achieved with a judicious choice of electron diffraction techniques, and the efficiency and accuracy of DP processing, which requires the development of new algorithms. Here, we review recent progress made in data collection, new algorithms, and automated electron DP analysis. These progresses will be highlighted using application examples in materials research. Future opportunities based on smart sampling and machine learning are also discussed.

Cepstral scanning transmission electron microscopy imaging of severe lattice distortions.

The development of four-dimensional (4D) scanning transmission electron microscopy (STEM) using fast detectors has opened-up new avenues for addressing some of longstanding challenges in electron imaging. One of these challenges is how to image severely distorted crystal lattices, such as at a dislocation core. Here we develop a new 4D-STEM technique, called Cepstral STEM, for imaging disordered crystals using electron diffuse scattering. In contrast to analysis based on Bragg diffraction, which measures the average and periodic scattering potential, electron diffuse scattering can detect fluctuations caused by crystal disorder. Local fluctuations of diffuse scattering are captured by scanning electron nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron diffuse scattering are detected through Cepstral analysis and used for imaging. By integrating Cepstral analysis with 4D-STEM, we demonstrate that information about the distortive part of electron scattering potential can be separated and imaged at nm spatial resolution. We apply the technique to the analysis of a dislocation core in SiGe and lattice distortions in a high entropy alloy.

Two-dimensional superconductivity and anisotropic transport at KTaO3 (111) interfaces.

The distinctive electronic structure found at interfaces between materials can allow unconventional quantum states to emerge. Here we report on the discovery of superconductivity in electron gases formed at interfaces between (111)-oriented KTaO3 and insulating overlayers of either EuO or LaAlO3 The superconducting transition temperature, as high as 2.2 kelvin, is about one order of magnitude higher than that of the LaAlO3/SrTiO3 system. Notably, similar electron gases at KTaO3 (001) interfaces remain normal down to 25 millikelvin. The critical field and current-voltage measurements indicate that the superconductivity is two-dimensional. In EuO/KTaO3 (111) samples, a spontaneous in-plane transport anisotropy is observed before the onset of superconductivity, suggesting the emergence of a distinct "stripe"-like phase, which is also revealed near the critical field.