Nonequivalent Atomic Vibrations at Interfaces in a Polar Superlattice.

In heterostructures made from polar materials, e.g., AlN-GaN-AlN, the nonequivalence of the two interfaces is long recognized as a critical aspect of their electronic properties; in that, they host different 2D carrier gases. Interfaces play an important role in the vibrational properties of materials, where interface states enhance thermal conductivity and can generate unique infrared-optical activity. The nonequivalence of the corresponding interface atomic vibrations, however, is not investigated so far due to a lack of experimental techniques with both high spatial and high spectral resolution. Herein, the nonequivalence of AlN-(Al0.65Ga0.35)N and (Al0.65Ga0.35)N-AlN interface vibrations is experimentally demonstrated using monochromated electron energy-loss spectroscopy in the scanning transmission electron microscope (STEM-EELS) and density-functional-theory (DFT) calculations are employed to gain insights in the physical origins of observations. It is demonstrated that STEM-EELS possesses sensitivity to the displacement vector of the vibrational modes as well as the frequency, which is as critical to understanding vibrations as polarization in optical spectroscopies. The combination enables direct mapping of the nonequivalent interface phonons between materials with different phonon polarizations. The results demonstrate the capacity to carefully assess the vibrational properties of complex heterostructures where interface states dominate the functional properties.

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell.

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy.

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom-atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. This work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials.

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

Selective Catalytic Behavior Induced by Crystal-Phase Transformation in Well-Defined Bimetallic Pt-Sn Nanocrystals.

The Pt-Sn bimetallic system is a much studied and commercially used catalyst for propane dehydrogenation. The traditionally prepared catalyst, however, suffers from inhomogeneity and phase separation of the active Pt-Sn phase. Colloidal chemistry offers a route for the synthesis of Pt-Sn bimetallic nanoparticles (NPs) in a systematic, well-defined, tailored fashion over conventional methods. Here, the successful synthesis of well-defined ≈2 nm Pt, PtSn, and Pt3 Sn nanocrystals with distinct crystallographic phases is reported; hexagonal close packing (hcp) PtSn and fcc Pt3 Sn show different activity and stability depending on the hydrogen-rich or poor environment in the feed. Moreover, face centred cubic (fcc) Pt3 Sn/Al2 O3 , which exhibited the highest stability compared to hcp PtSn, shows a unique phase transformation from an fcc phase to an L12 -ordered superlattice. Contrary to PtSn, H2 cofeeding has no effect on the Pt3 Sn deactivation rate. The results reveal structural dependency of the probe reaction, propane dehydrogenation, and provide a fundamental understanding of the structure-performance relationship on emerging bimetallic systems.

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.