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Jinsub Park

Publications and source records attributed to Jinsub Park.

3 recordsLinked to original sources

Interplay between Interlayer Shift and Twist: Twisted van der Waals Nanowires Driven by Rotational Twinning

In van der Waals(vdW) layered materials, interlayer shift and twist have enabled the control of material properties through polytype and moire engineering. Various approaches, including bottom-up synthesis and manual layer-by-layer stacking, have been utilized to engineer targeted stacking configurations. However, the interplay between interlayer shift and twist, as well as reliable mechanisms for fine-tuning these parameters, remains largely unexplored. Here, we report a previously unrecognized twisting mechanism arising from preferred tilted stacking and twinning in vdW crystals. Electron diffraction and atomic-resolution scanning transmission electron microscopy(STEM) imaging reveal that the lattice planes of group-IV chalcogenide GeSe_{2-x}Te_x rotate continuously along the nanowire growth axis, with twist rates depending systematically on nanowire radius. Atomic-scale imaging further identifies a continuous rotational twin boundary extending along the central region of the nanowire. First-principles calculations and structural relaxation simulations confirm that the twisting deformation originates from energetic competition between the preferred interlayer stacking registry and the strain cost imposed by rotational twinning. These findings establish rotational twinning as an intrinsic route to spontaneous twist formation and provide a design principle for realizing twist-engineered vdW crystals with compatible crystal symmetries and stacking motifs.

cond-mat.mtrl-sci

Electrical transport properties driven by unique bonding configuration in gamma-GeSe

Group-IV monochalcogenides have recently shown great potential for their thermoelectric, ferroelectric, and other intriguing properties. The electrical properties of group-IV monochalcogenides exhibit a strong dependence on the chalcogen type. For example, GeTe exhibits high doping concentration, whereas S/Se-based chalcogenides are semiconductors with sizable bandgaps. Here, we investigate the electrical and thermoelectric properties of gamma-GeSe, a recently identified polymorph of GeSe. gamma-GeSe exhibits high electrical conductivity (~106 S/m) and a relatively low Seebeck coefficient (9.4 uV/K at room temperature) owing to its high p-doping level (5x1021 cm-3), which is in stark contrast to other known GeSe polymorphs. Elemental analysis and first-principles calculations confirm that the abundant formation of Ge vacancies leads to the high p-doping concentration. The magnetoresistance measurements also reveal weak-antilocalization because of spin-orbit coupling in the crystal. Our results demonstrate that gamma-GeSe is a unique polymorph in which the modified local bonding configuration leads to substantially different physical properties.

cond-mat.mtrl-sci

STEM image analysis based on deep learning: identification of vacancy defects and polymorphs of ${MoS_2}$

Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of ${MoS_2}$ from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.

cond-mat.mes-hall