arXiv subjects

Juhyeok Lee

Publications and source records attributed to Juhyeok Lee.

11 recordsLinked to original sources

arXiv2026

Mesoscale Crystallographic Helicity in Confined Tellurium Quantum Wires

Helical order can facilitate symmetry breaking and emergent physical responses in crystalline materials, yet how intrinsic chirality manifests beyond atomic length scales remains poorly understood. Here, the direct observation and quantitative characterization of long-range crystallographic helicity in template-grown tellurium (Te) quantum wires on amorphous substrates are reported. Four-dimensional scanning transmission electron microscopy (4D-STEM) enables quantitative mapping of crystallographic orientation with nanometer-scale spatial resolution. The resulting orientation maps establish continuous mesoscale lattice twisting, providing direct evidence of long-range crystallographic helicity. Correlated orientation and strain mapping reveal pronounced lateral strain heterogeneity, with compressive strain concentrated within the wire interior. Systematic analysis across multiple wires suggests that higher twist rates are generally associated with weaker lateral compressive strain, narrower wires, and better atomic chain - template axis alignment. Complementary first-principles calculations on finite Te nanorods further suggest that twisting is intrinsically accessible in nucleus-scale Te clusters and strain can bias the preferred torsional state. Together, these results support a growth-incorporated, strain-biased picture in which nanoscale confinement and anisotropic strain facilitate torsional relaxation and stabilize mesoscale helicity in Te nanostructures highlighting strain and confinement as potential routes for engineering chiral lattice states in van der Waals nanostructures.

cond-mat.mtrl-sci↗

arXiv2026

Hydration-controlled twist forms a moiré glass in charge-frustrated layered silicates

Twisting layered materials produces moiré superlattices, but prescribed twist angles are usually obtained by demanding assembly procedures. Here we show that montmorillonite, an abundant swelling clay, forms tunable moiré superlattices naturally. Focal-series high-resolution transmission electron microscopy, geometric phase analysis, and molecular dynamics simulation reveal that its apparent rotational disorder is biased toward low-angle misorientations inherited from discrete hydration states. Multilayer stacks preferentially adopt twists near 1-2°, 4°, and 10°, producing long-wavelength moirés without long-range rotational order. We define this kinetically trapped state as a moiré glass, distinct from featureless turbostratic stacking. Simulations indicate that lattice-charge disorder stabilizes the angular preferences, whereas charge ordering promotes random stacking. Hydration screens interlayer interactions and lubricates twist, while dehydration arrests the resulting configurations in discrete steps. These results establish dynamic hydration as a macroscopic handle for programming twist in layered matter.

cond-mat.mtrl-sci↗

arXiv2026

PhaseT3M: 3D Imaging at 1.6 Å Resolution via Electron Cryo-Tomography with Nonlinear Phase Retrieval

Electron cryo-tomography (cryo-ET) enables 3D imaging of complex, radiation-sensitive structures with molecular detail. However, image contrast from the interference of scattered electrons is nonlinear with atomic density and multiple scattering further complicates interpretation. These effects degrade resolution, particularly in conventional reconstruction algorithms, which assume linearity. Particle averaging can reduce such issues but is unsuitable for heterogeneous or dynamic samples ubiquitous in biology, chemistry, and materials sciences. Here, we develop a phase retrieval-based cryo-ET method, PhaseT3M. We experimentally demonstrate its application to a ~7 nm Co3O4 nanoparticle on ~30 nm carbon substrate, achieving a maximum resolution of 1.6 Å, surpassing conventional limits using standard cryo-TEM equipment. PhaseT3M uses a multislice model for multiple scattering and Bayesian optimization for alignment and computational aberration correction, with a positivity constraint to recover 'missing wedge' information. Applied directly to biological particles, it enhances resolution and reduces artifacts, establishing a new standard for routine 3D imaging of complex, radiation-sensitive materials.

cond-mat.mtrl-sci↗

arXiv2025

Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction

Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systematic optimization remains incomplete, as it requires an atomic-scale understanding of these factors and their dynamics over potential cycling, as well as their relationship to ORR activity. Here, we implement neural network-assisted atomic electron tomography to measure the 3D atomic structural dynamics and their effects on the functional degradation of PtNi alloy catalysts. Our results reveal that PtNi catalysts undergo shape changes, surface alloying, and strain relaxation during cycling, which can be effectively mitigated by Ga doping. By combining geometry, local chemistry, and strain analysis, we calculated the changes in ORR activity over thousands of cycles and observed that Ga doping leads to higher initial activity and greater stability. These findings offer a pathway to understanding 3D atomic structural dynamics and their relation to ORR activity during cycling, paving the way for the systematic design of durable, high-efficiency nanocatalysts.

cond-mat.mtrl-sci↗

arXiv2025

Advancing atomic electron tomography with neural networks

Accurate determination of three-dimensional (3D) atomic structures is crucial for understanding and controlling the properties of nanomaterials. Atomic electron tomography (AET) offers non-destructive atomic imaging with picometer-level precision, enabling the resolution of defects, interfaces, and strain fields in 3D, as well as the observation of dynamic structural evolution. However, reconstruction artifacts arising from geometric limitations and electron dose constraints can hinder reliable atomic structure determination. Recent progress has integrated deep learning, especially convolutional neural networks, into AET workflows to improve reconstruction fidelity. This review highlights recent advances in neural network-assisted AET, emphasizing its role in overcoming persistent challenges in 3D atomic imaging. By significantly enhancing the accuracy of both surface and bulk structural characterization, these methods are advancing the frontiers of nanoscience and enabling new opportunities in materials research and technology.

cond-mat.mtrl-sci↗

arXiv2023

Revealing the Three-Dimensional Arrangement of Polar Topology in Nanoparticles

In the early 2000s, low dimensional systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and elastic distortion energies. However, the measurement and classification of topological textures for general ferroelectric nanostructures have remained elusive, as it requires mapping the local polarization at the atomic scale in three dimensions. Here we unveil topological polar structures in ferroelectric BaTiO3 nanoparticles via atomic electron tomography, which enables us to reconstruct the full three-dimensional arrangement of cation atoms at an individual atom level. Our three-dimensional polarization maps reveal clear topological orderings, along with evidence of size-dependent topological transitions from a single vortex structure to multiple vortices, consistent with theoretical predictions. The discovery of the predicted topological polar ordering in nanoscale ferroelectrics, independent of epitaxial strain, widens the research perspective and offers potential for practical applications utilizing contact-free switchable toroidal moments.

cond-mat.mtrl-sci↗

arXiv2022

Multislice Electron Tomography using 4D-STEM

Electron tomography offers important three-dimensional (3D) structural information which cannot be observed by two-dimensional imaging. By combining annular dark field scanning transmission electron microscopy (ADF-STEM) with aberration correction, the resolution of electron tomography has reached atomic resolution. However, tomography based on ADF-STEM inherently suffers from several issues, including a high electron dose requirement, poor contrast for light elements, and artifacts from image contrast nonlinearity. Here, we developed a new method called MultiSlice Electron Tomography (MSET) based on 4D-STEM tilt series. Our simulations show that multislice-based 3D reconstruction can effectively reduce undesirable reconstruction artifacts from the nonlinear contrast, allowing precise determination of atomic structures with improved sensitivity for low-Z elements, at considerably low electron dose conditions. We expect that the MSET method can be applied to a wide variety of materials, including radiation-sensitive samples and materials containing light elements whose 3D atomic structures have never been fully elucidated due to electron dose limitations or nonlinear imaging contrast.

cond-mat.mtrl-sci↗

arXiv2022

Revealing 3-dimensional core-shell interface structures at the single-atom level

Nanomaterials with core-shell architectures are prominent examples of strain-engineered materials, where material properties can be designed by fine-tuning the misfit strain at the interface. Here, we elucidate the full 3D atomic structure of Pd@Pt core-shell nanoparticles at the single-atom level via atomic electron tomography. Full 3D displacement fields and strain profiles of core-shell nanoparticles were obtained, which revealed a direct correlation between the surface and interface strain. It also showed clear Poisson effects at the scale of the full nanoparticle as well as the local atomic bonds. The strain distributions show a strong shape-dependent anisotropy, whose nature was further corroborated by molecular statics simulations. From the observed surface strains, the surface oxygen reduction reaction activities were predicted. These findings give a deep understanding of structure-property relationships in strain-engineerable core-shell systems, which could pave a new way toward direct control over the resulting catalytic properties.

cond-mat.mtrl-sci↗

arXiv2021

Direct observation of three-dimensional atomic structure of twinned metallic nanoparticles and their catalytic properties

We determined a full 3D atomic structure of a dumbbell-shaped Pt nanoparticle formed by a coalescence of two nanoclusters using deep learning assisted atomic electron tomography. Formation of double twin boundary was clearly observed at the interface, while substantial anisotropy and disorder were also found throughout the nanodumbbell. This suggests that the diffusion of interfacial atoms mainly governed the coalescence process, but other dynamic processes such as surface restructuring and plastic deformation were also involved. A full 3D strain tensor was clearly mapped, which allows direct calculation of the oxygen reduction reaction activity at the surface. Strong tensile strain was found at the protruded region of the nanodumbbell, which results in an improved catalytic activity on {100} facets. This work provides important clues regarding the coalescence mechanism and the relation between the atomic structure and catalytic property at the single-atom level.

cond-mat.mtrl-sci↗

arXiv2021

Hund's physics and the magnetic ground state of CrOX (X = Cl, Br)

To understand the magnetic property of layered van der Waals materials CrOX (X = Cl, Br), we performed the detailed first-principles calculations for both bulk and monolayer. We found that the charge-only density functional theory combined with the explicit on-site interaction terms (so-called cDFT$+U$) well reproduces the experimental magnetic ground state of bulk CrOX, which is not the case for the use of spin-dependent density functional (so-called sDFT$+U$). Unlike some of the previous studies, our results show that CrOX monolayers are antiferromagnetic as in the bulk. It is also consistent with our magnetic force linear response calculation of exchange couplings $J_{\rm ex}$. The result of orbital-decomposed $J_{\rm ex}$ calculations shows that the Cr $t_\textrm{2g}$-$t_\textrm{2g}$ component mainly contributes to the antiferromagnetic order in both bulk and monolayer. Our result and analysis show that taking the correct Hund's physics into account is of key importance to construct the magnetic phase diagram and to describe the electronic structure.

cond-mat.mtrl-sci↗

arXiv2020

Single-atom level determination of 3-dimensional surface atomic structure via neural network-assisted atomic electron tomography

Functional properties of nanomaterials strongly depend on their surface atomic structure, but they often become largely different from their bulk structure, exhibiting surface reconstructions and relaxations. However, most of the surface characterization methods are either limited to 2-dimensional measurements or not reaching to true 3D atomic-scale resolution, and single-atom level determination of the 3D surface atomic structure for general 3D nanomaterials still remains elusive. Here we show the measurement of 3D atomic structure of a Pt nanoparticle at 15 pm precision, aided by a deep learning-based missing data retrieval. The surface atomic structure was reliably measured, and we find that <100> and <111> facets contribute differently to the surface strain, resulting in anisotropic strain distribution as well as compressive support boundary effect. The capability of single-atom level surface characterization will not only deepen our understanding of the functional properties of nanomaterials but also open a new door for fine tailoring of their performance.

cond-mat.mtrl-sci↗