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The concept of remote epitaxy involves a two-dimensional van der Waals layer covering the substrate surface, which still enable adatoms to follow the atomic motif of the underlying substrate. The mode of growth must be carefully defined as defects, e.g., pinholes, in two-dimensional materials can allow direct epitaxy from the substrate, which, in combination with lateral epitaxial overgrowth, could also form an epilayer. Here, we show several unique cases that can only be observed for remote epitaxy, distinguishable from other two-dimensional material-based epitaxy mechanisms. We first grow BaTiO₃on patterned graphene to establish a condition for minimizing epitaxial lateral overgrowth. By observing entire nanometer-scale nuclei grown aligned to the substrate on pinhole-free graphene confirmed by high-resolution scanning transmission electron microscopy, we visually confirm that remote epitaxy is operative at the atomic scale. Macroscopically, we also show variations in the density of GaN microcrystal arrays that depend on the ionicity of substrates and the number of graphene layers. 

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems. 

Abstract Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area. Here, we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials. Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy (4D-STEM). Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample. Using this purely data-driven analysis, we have uncovered different types of material deformations, such as strain, lattice distortion, bending contour, etc., which can significantly impact the band structure and subsequent performance of nanomaterials-based devices. We envision that this data-driven procedure will provide insight into materials’ intrinsic structures and accelerate the discovery of materials.