Jun Yeon Hwang

Integrating boron nitride nanotubes (BNNTs) into robust macroscopic architectures for deep space radiation shielding remains a fundamental challenge. Here, we report continuous self-assembled all-nanotube (SAAN) fibers with an 80 wt% BNNT loading, combining tensile strength exceeding 1 GPa with extreme thermal stability from −196 to 1200 °C. These fibers originate from the thermodynamic co-assembly of BNNTs and carbon nanotubes (CNTs) into a lyotropic liquid crystalline mesophase. This shear-responsive dope enables cooperative reorientation and dense nanotube packing during wet-spinning. When assembled into macroscopic textiles, SAAN exhibits an exceptional thermal neutron attenuation coefficient of 2.85 mm⁻¹. Furthermore, computational simulations indicate these lightweight textiles can effectively mitigate highly penetrating secondary thermal neutrons generated by Galactic Cosmic Ray (GCR) spallation. Demonstrating their seamless integration into commercial 2D flat and 3D tubular textile manufacturing processes, this nanomanufacturing approach provides a scalable and reliable strategy for space radiation shielding in both intravehicular (IVA) and extravehicular (EVA) activities.

Decoupling superposed force vectors along spatially distinct axes is a prerequisite for artificial somatosensation in soft intelligent systems. Although polymer‐piezoelectric composites offer essential compliance, randomly distributed nanofillers result in a loss of inherent polar directionality and macroscopic anisotropy, which prevents force‐vector discrimination. Here, we address this challenge by encoding force‐mode selectivity into magneto‐piezoelectric composites via field‐programmed anisotropy. Guided by a rigorous torque‐balance framework, we achieve the deterministic spatial alignment of Fe ₃ O ₄ ‐decorated BaTiO ₃ nanowires within a shape‐memory polymer matrix. The resulting axially aligned hierarchical percolation networks serve as continuous load‐transfer pathways to concentrate mechanical stress along the principal axes of the nanowires while suppressing off‐axis interference. Such structure‐driven anisotropy in electromechanical coupling maximizes piezoelectric transduction efficiency and empowers the nanocomposite to intrinsically distinguish force modalities. By upscaling the intrinsic anisotropy of piezoelectric nanowires to the macroscopic composite level, our approach defines a physical basis for material‐encoded mechanoperception. Our findings establish a versatile platform for soft embodied intelligence that offers vector‐resolved somatosensory capabilities at the material level.

Giga‐voxel digital models offer abundant geometric detail; however, no mainstream method currently exists to efficiently distribute individual voxels across massive image volumes, and designing complex anisotropic composite materials remains infeasible due to the absence of promising methods. Herein, we propose a systematic digital twin workflow tailored for generating high‐fidelity virtual representations of anisotropic composite microstructures and giga‐voxel meso‐structural models, leveraging a harmonious integration of top‐down image‐based modeling and bottom‐up data‐driven generation. Our study demonstrates the efficacy of micro‐digital representations as foundational building blocks within a continuum of digital assembly processes tailored for mesostructural models. Utilizing 3D image data, specifically X‐ray tomography, our data‐driven modeling meticulously characterizes the geometric attributes of the experimentally observed objects, thereby facilitating the creation of digital unit twins, each endowed with distinct identities assigned through a random seed generation. The closed‐loop system provides feedback mechanism between data and model to ensure the 3D quality of the generated models. For hierarchical organization at the giga‐voxel level, the digital unit twins are methodically expanded into cohesive 3D architectures based on assembly relationship at length scales of more than four orders of magnitude. Remarkably, this hierarchical model provides intricate insight into micro‐to‐macro geometrics while preserving the intrinsic microstructure.

Three-dimensional printing is a highly promising manufacturing technology that enables easy production of complex shapes. Composite lattice structures are highly efficient, having the advantages of fiber-reinforced composites and the excellent structural performance of lattice configurations. Highly efficient structures can be developed by combining the benefits of 3D printing and composite lattice structures. This study examined the effect of printing path and axial angle in joint areas on the compressive strength of composite lattice unit cells fabricated via continuous fiber 3D printing. Compression tests were conducted to analyze deformation, failure modes, and causes of failure. A finite element model was used to analyze buckling behavior and establish design criteria. Results showed that the printing path significantly influenced failure mode and strength, while a fabrication method without a defect at the joint was important for improving structural performance. Finally, design criteria, in terms of the knockdown factor and in-plane bifurcation buckling behavior, were developed based on experimental and numerical results.

The discovery of graphene as a two-dimensional material through graphite exfoliation marked a significant starting point for the research and development of graphene-related materials [ 1]. These graphene-related materials are designed to leverage graphene’s unique properties, such as high electrical and thermal conductivity and robust mechanical strength, which are attributed to its crystal structure. Therefore, maintaining the structural integrity of this crystal lattice is crucial [ 2]. However, various production methods and experiments have been conducted on graphene-related materials, but achieving a flawless crystal structure remains an elusive goal. Nevertheless, electron diffraction experiments using transmission electron microscopy (TEM) demonstrated that many areas within the fabricated material contain multiple, independently existing regions of perfect graphene crystals, consisting solely of carbon atoms and entirely free of defects [ 3]. To directly observe these atomic-level crystal and defect structures, TEM-based analysis techniques have been employed. Initially, high-resolution TEM (HR-TEM) with spherical aberration correction was used for phase contrast imaging [ 4]. However, due to the complexity involved in interpreting phase-contrast images – which requires several steps, including simulation –, the combination of high-angle annular dark-field (HAADF) imaging in scanning transmission electron microscopy (STEM) with spectroscopic analysis is now widely adopted. In this presentation, we present case studies analyzing various graphene-related materials using HR-TEM imaging and energy dispersive x-ray spectroscopy (EDS) and electron energy loss spectroscopy (EELS) in STEM mode. Thickness studies of graphene-related materials employed HR-TEM imaging techniques, diffraction analysis, and STEM-EELS spectroscopy, with cross-verification through macroscopic analytical methods such as x-ray diffraction (XRD), Raman spectroscopy, atomic force microscopy (AFM), and x-ray photoelectron spectroscopy (XPS).

Materials scientists have taken a learn-from-nature approach to study the structure-property relationships of natural materials. Here we introduce a nature-inspired composite architecture showing a hierarchical assembly of granular-like building blocks with specific topological textures. The structural complexity of the resulting architecture is advanced by applying the concept of grain orientation internally to each building block to induce a tailored crack resistance. Hexagonal grain-shaped building blocks are filled with parallel-oriented filament bundles, and these function as stiff-blocks with high anisotropy due to the embedded fiber reinforcements. Process-induced interfacial voids, which provide preferential crack paths, are strategically integrated with cracks to improve fracture toughness at the macroscopic scale. This study discusses the structural effects of the local/global orientations, stacking sequences, feature sizes, and gradient assemblies of granular blocks on crack tolerance behavior. Alternating stacking sequences induce cracks propagating in the arrestor direction, which boost the fracture energy up to 2.4 times higher than the same layup stacking sequence. Gradient arrangements of feature sizes from coarse to fine or fine to coarse result in the coexistence of stiffness and toughness. Our approach to applying crystallographic concepts to complex composite architectures inspires for original models of fracture mechanics.