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Ni, Haoyang

Publications and source records attributed to Ni, Haoyang.

Real-space visualization of atomic displacements in a long-wavelength charge density wave using cryogenic 4D-STEM

EuA⁢l 4 , a known skyrmion magnet, represents a class of materials that show complex electronic behaviors involving long-wavelength charge density wave (CDW). Here we realize direct real-space imaging of the concomitant periodic lattice distortion (PLD) with picometer precision in EuA⁢l 4 . Simultaneous imaging of microstructure, polarization, and interatomic distance is made possible using cryogenic four-dimensional scanning transmitting electron microscopy (cryo-4DSTEM). With this approach, we determined that the PLD in EuA⁢l 4 incorporates two out-of-phase atomic displacement modes on the Eu and Al sublattices. The PLD introduces periodic local symmetry fluctuations from the average symmetry of the bulk EuA⁢l 4 crystal. This critical local structural insight helps understanding of charge-spin-lattice couplings behind the intertwined quantum order in EuA⁢l 4 . Furthermore, our results also demonstrate cryo-4DSTEM as an indispensable approach for probing CDW and related quantum materials at low temperatures.

Ni, Haoyang↗

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–(Al 0.65 Ga 0.35 )N and (Al 0.65 Ga 0.35 )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. Furthermore, the results demonstrate the capacity to carefully assess the vibrational properties of complex heterostructures where interface states dominate the functional properties.

36 MATERIALS SCIENCE↗

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, in this work, 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.

four-dimensional scanning transmission electron mi↗

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. Here, 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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗