Author: H.Kim†, H.Lee†, H.Son, I.Bae, J.Choi and J.Kim*Title: Ab initio calculations of Nb-based MAX phases as bond coats for thermal barrier coatingsJournal: Journal of Materials Research and TechnologyYear: 2024Impact factor: 6.2Abstract:Thermal barrier coatings (TBCs) are essential to the reliable high-temperature operation of gas turbines and engines. They comprise a ceramic top coat (TC), a metallic bond coat (BC), and a superalloy substrate. Metallic BCs are common, but they require a minimal difference in the coefficient of thermal expansion (CTE) between the TC and substrate. Among ceramic materials, MAX phases have high CTE. This study reports our use of ab initio calculations to assess the suitability of MAX phases as TBCs. We model Nb-based MAX phases with 211 and 312 structures that have Al or Si at the A sites and C or N at the X sites. We use the quasi-harmonic approximation to calculate the Young’s moduli and CTEs of the materials with respect to temperature. The Nb2SiN MAX phase appears as the most suitable for use as a BC between various ceramic TCs and an Inconel-718 substrate. It is predicted to be effective in relieving thermal stresses due to its high CTE of 10.882 × 10−⁶ K−1 at 1,273 K. The results indicate that carbide MAX phases with high Young’s modulus and low CTE should be used with caution. They may not accommodate thermal stresses as effectively, potentially leading to material failure or reduced performance. Overall, our study indicates the potential of Nb-based MAX structures for use as BCs.

Author: J.Park†, J.Kim†, S.Lee†, H.Kim†, H.Lim, J.Park, T.Yun, J.Lee, S.Kim, H.Jin, K.Park, H.Kang, H.Kim, H.Jin, J.Kim*, S.Kim* and B.Kim*Title: 2D MoS₂ Helical Liquid Crystalline Fibers for Multifunctional Wearable SensorsJournal: Advanced Fiber MaterialsYear: 2024Impact factor: 17.2Abstract:Fiber-based material systems are emerging as key elements for next-generation wearable devices due to their remarkable advantages, including large mechanical deformability, breathability, and high durability. Recently, greatly improved mechanical stability has been established in functional fiber systems by introducing atomic-thick two-dimensional (2D) materials. Further development of intelligent fibers that can respond to various external stimuli is strongly needed for versatile applications. In this work, helical-shaped semiconductive fibers capable of multifunctional sensing are obtained by wet-spinning MoS2 liquid crystal (LC) dispersions. The mechanical properties of the MoS2 fibers were improved by exploiting high-purity LC dispersions consisting of uniformly-sized MoS2 nanoflakes. Notably, three-dimensional (3D) helical fibers with structural chirality were successfully constructed by controlling the wet-spinning process parameters. The helical fibers exhibited multifunctional sensing characteristics, including (1) photodetection, (2) pH monitoring, (3) gas detection, and (4) 3D strain sensing. 2D materials with semiconducting properties as well as abundant surface reactive sites enable smart multifunctionalities in one-dimensional (1D) and helical fiber geometry, which is potentially useful for diverse applications such as wearable internet of things (IoT) devices and soft robotics.

Author: M.Kim†, J.Kim†, H.Kim and J.Kim*Title: High-Throughput Data-Driven Machine Learning Prediction of Thermal Expansion Coefficients of High-Entropy Solid Solution CarbidesJournal: International Journal of Refractory Metals and Hard MaterialsYear: 2024Impact factor: 3.6Abstract:Recent advances in machine learning and the expanding availability of materials data have enabled significant developments in materials science. In this study, novel configurations of high-entropy ceramic (HEC) materials were explored by predicting their coefficient of thermal expansion (CTE) using machine learning (ML) and high-throughput screening. A machine learning model was built using 3360 datasets containing the thermodynamic, elastic, and thermophysical properties of HEC with carbide configurations of (Ti0.2Ta0.2 × 0.2Y0.2Z0.2)C. The high correlation of the bulk and Young's moduli, and cohesive energy features with the CTE facilitated its prediction. The random forest (RF) and neural network (NET)-based models successfully reproduced the CTE reported in existing experimental and theoretical studies. Overall, first-principles calculation was implemented to configure a database for HEC and a new ML application method is proposed.

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