Author: J.Lee†, H.Son†, H.Kim, J.Choi, H.Kim, J.Kim*, Y.Choa*, S.Moon*Title: Ultralight SiC Nanowire Aerogels with Exceptional Thermal Stability Enabled by Diffusion-Controlled Microstructure EngineeringJournal: Ceramics InternationalYear: 2026Impact factor: 5.6Abstract:Uniform formation of three-dimensional SiC nanowire (SiC NW) aerogels remains challenging because ceramic conversion in bulk architectures is often constrained by limited vapor diffusion. Here, controlled microstructural design of polyimide-derived carbon aerogels is shown to regulate vapor transport during carbothermic reactions, enabling systematic examination of how internal architecture influences nanowire nucleation and conversion depth. Open, low-density scaffolds allow homogeneous vapor penetration throughout the bulk, leading to continuous and fully interconnected SiC NW networks, whereas denser architectures restrict diffusion and confine SiC formation to surface regions. As a result, the optimized SiC NW aerogel exhibits an ultralow density of 0.023 g·cm-3, high porosity (∼98%), and a low thermal conductivity of 0.046 W·m-1·K-1. The material further shows excellent thermal and oxidative stability, maintaining a temperature gradient exceeding 800 °C under prolonged direct flame exposure (>1000 °C). These findings indicate that microstructure-guided control of diffusion offers a broadly applicable route for designing ceramic nanowire aerogels for extreme thermal insulation.

박사과정으로 졸업하신 김명재 박사님이 현대제철주식회사 AI·BIGDATA 페스티벌에서 장려상을 수상하셨습니다. 축하드립니다~~!

Author: H.Kim†, J.Kim†, K.Lee, H.Son, J.Choi, I.Bae, H.Lee, S.Kyung, J.Kim*Title: DFT-verified database and machine learning framework for designing high entropy carbide with superior mechanical propertiesJournal: materials today communicationsYear: 2026Impact factor: 4.5Abstract:High-entropy carbides (HECs) represent a new class of materials that combine multiple principal elements into a single-phase structure, exhibiting exceptional mechanical performance such as high hardness, thermal stability, and wear resistance. However, the extensive compositional space of HECs poses a significant challenge for conventional experimental and computational discovery. In this study, we develop an enhanced crystal graph convolutional neural network (CGCNN) model capable of predicting key elastic properties, specifically bulk and Young’s moduli, directly from crystal structures. By incorporating computational data for solid solutions into the training dataset, the model achieves superior accuracy and generalizability across diverse HEC compositions. Our DFT-verified dataset ensured high reliability and significantly improved prediction performance (R2 = 0.98 vs. 0.92). This highlights the importance of data quality in achieving robust and accurate ML models for materials design. The proposed model successfully identifies five high-performance HEC compositions. These findings demonstrate the capability of machine learning–driven approaches to accelerate HEC discovery and design, offering a cost-effective and efficient pathway to optimize mechanical properties for advanced applications.

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