On-column 2p bound state with topological charge \pm1 excited by an atomic-size vortex beam in an aberration-corrected scanning transmission electron microscope

Atomic-size vortex beams have great potential in probing materials' magnetic moment at atomic scales. However, the limited depth of field of vortex beams constrains the probing depth in which the helical phase front is preserved. On the other hand, electron channeling in crystals can counteract beam divergence and extend the vortex beam without disrupting its topological charge. Specifically, in this paper, we report atomic vortex beams with topological charge \pm1 can be coupled to the 2p columnar bound states and propagate for more 50 nm without being dispersed and losing its helical phase front. We gave numerical solutions to the 2p columnar orbitals and tabulated the characteristic size of the 2p states of two typical elements, Co and Dy, for various incident beam energies and various atomic densities. The tabulated numbers allow estimates of the optimal convergence angle for maximal coupling to 2p columnar orbital. We also have developed analytic formulae for beam energy, convergence-angle, and hologram dependent scaling for various characteristic sizes. These length scales are useful for the design of pitch-fork apertures and operations of microscopes in the vortex-beam imaging mode.Comment: 30 pages, 7 figures, Microscopy and Microanalysis, in pres

Valence-programmable nanoparticle architectures.

Nanoparticle-based clusters permit the harvesting of collective and emergent properties, with applications ranging from optics and sensing to information processing and catalysis. However, existing approaches to create such architectures are typically system-specific, which limits designability and fabrication. Our work addresses this challenge by demonstrating that cluster architectures can be rationally formed using components with programmable valence. We realize cluster assemblies by employing a three-dimensional (3D) DNA meshframe with high spatial symmetry as a site-programmable scaffold, which can be prescribed with desired valence modes and affinity types. Thus, this meshframe serves as a versatile platform for coordination of nanoparticles into desired cluster architectures. Using the same underlying frame, we show the realization of a variety of preprogrammed designed valence modes, which allows for assembling 3D clusters with complex architectures. The structures of assembled 3D clusters are verified by electron microcopy imaging, cryo-EM tomography and in-situ X-ray scattering methods. We also find a close agreement between structural and optical properties of designed chiral architectures

Extended Depth of Field for High Resolution Scanning Transmission Electron Microscopy

Aberration-corrected scanning transmission electron microscopes (STEM) provide sub-angstrom lateral resolution; however, the large convergence angle greatly reduces the depth of field. For microscopes with a small depth of field, information outside of the focal plane quickly becomes blurred and less defined. It may not be possible to image some samples entirely in focus. Extended depth-of-field techniques, however, allow a single image, with all areas in-focus, to be extracted from a series of images focused at a range of depths. In recent years, a variety of algorithmic approaches have been employed for bright field optical microscopy. Here, we demonstrate that some established optical microscopy methods can also be applied to extend the ~6 nm depth of focus of a 100 kV 5th-order aberration-corrected STEM (alpha_max = 33 mrad) to image Pt-Co nanoparticles on a thick vulcanized carbon support. These techniques allow us to automatically obtain a single image with all the particles in focus as well as a complimentary topography map.Comment: Accepted, Microscopy and Microanalysi

Human Perception-Inspired Grain Segmentation Refinement Using Conditional Random Fields

Accurate segmentation of interconnected line networks, such as grain boundaries in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating intricate post-processing for effective contour closure and continuity. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique, leveraging domain knowledge about grain boundary connectivity and employing conditional random fields and perceptual grouping rules. This approach significantly enhances segmentation mask accuracy, achieving a 79% segment identification accuracy in validation with a U-Net model on electron microscopy images of a polycrystalline oxide. Additionally, a novel grain alignment metric is introduced, showing a 51% improvement in grain alignment, providing a more detailed assessment of segmentation performance for complex microstructures. This method not only enables rapid and accurate segmentation but also facilitates an unprecedented level of data analysis, significantly improving the statistical representation of grain boundary networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential