Results 41 to 50 of about 14,999,085 (143)

Xenotime-type high-entropy (Dy1/7Ho1/7Er1/7Tm1/7Yb1/7Lu1/7Y1/7)PO4: A promising thermal/environmental barrier coating material for SiCf/SiC ceramic matrix composites

Journal of Advanced Ceramics, 2023
Rare-earth phosphates (REPO4) are regarded as one of the promising thermal/environmental barrier coating (T/EBC) materials for SiCf/SiC ceramic matrix composites (SiC-CMCs) owing to their excellent resistance to water vapor and CaO–MgO–Al2O3–SiO2 (CMAS).
Peixiong Zhang, Xingjun Duan, Xiaochang Xie, Donghai Ding, Tao Yang, Xinmei Hou, Yunsong Zhao, Enhui Wang   +7 more
doaj   +1 more source

Rare-Earth-Zirconate Porous High-Entropy Ceramics with Unique Pore Structures for Thermal Insulating Applications

, 2023
Porous high-entropy ceramics are a new alternative material for thermal insulation. Their better stability and low thermal conductivity are due to lattice distortion and unique pore structures.
Xuanyu Meng, Ping Zhang, Jiatong Zhu, Hengchang Wang, Lang Lin, Feng Gao, Jie Xu   +6 more
core   +1 more source

High Entropy Materials: Challenges and Prospects

, 2021
This book is a reprint of a special issue of Metals (ISSN 2075-4701), titled High Entropy Materials: Challenges and Prospects. It is a compilation of nine articles from different aspects of high-entropy materials.

core   +1 more source

High-entropy spinel ferrites MFe2O4 (M = Mg, Mn, Fe, Co, Ni, Cu, Zn) with tunable electromagnetic properties and strong microwave absorption

Journal of Advanced Ceramics, 2022
Ferrites are the most widely used microwave absorbing materials to deal with the threat of electromagnetic (EM) pollution. However, the lack of sufficient dielectric loss capacity is the main challenge that limits their applications.
Jiabin Ma, Biao Zhao, Huimin Xiang, Fu-Zhi Dai, Yi Liu, Rui Zhang, Yanchun Zhou   +6 more
doaj   +1 more source

High‐Entropy Ceramic‐Enhanced Deformable Triboelectric Nanogenerator for Noncontact Biomechanical and Raindrop Energy Harvesting

Advanced Materials, EarlyView.
A large‐area, deformable high‐entropy‐ceramic (HEC)‐doped triboelectric nanogenerator with a trilayer architecture is developed, consisting of an HEC‐doped triboelectric layer, a graphite‐like textile charge‐storage layer, and stretchable carbon black electrodes.
Shengyou Li, Wei‐Chen Peng, Syun‐Hong Chou, Zhi‐Xian Yan, Jiann‐Yeu Chen, Dun‐Jie Jhan, Wei‐Chun Yang, Shi‐Hong Chen, Yi‐Lin Huang, Cheng‐Hung Tsai, Ming‐Yen Lu, Heng‐Jui Liu, Cheolmin Park, Ying‐Chih Lai   +13 more
wiley   +1 more source

Uncovering the thermal expansion in high-entropy ceramics by machine learning

Journal of Materiomics
Understanding the fundamental mechanisms of thermal expansion in high-entropy ceramics is crucial for their structural applications under extreme conditions.
Yiwen Liu, Hulei Yu, Lei Zhuang, Yanhui Chu   +3 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Evaluating the properties and applications of high entropy borides, nitrides and carbides

Next Materials
This study investigates the unique properties and prospective uses of high entropy ceramics, with special emphasis on borides (HEBs), carbides (HECbs), and nitrides (HENs).
Chika Oliver Ujah, Peter Apata Olubambi
doaj   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

High-entropy (Nd0.2Sm0.2Eu0.2Y0.2Yb0.2)4Al2O9 with good high temperature stability, low thermal conductivity, and anisotropic thermal expansivity

Journal of Advanced Ceramics, 2020
The critical requirements for the environmental barrier coating (EBC) materials of silicon-based ceramic matrix composites (CMCs) include good tolerance to harsh environments, thermal expansion matches with the interlayer mullite, good high-temperature ...
Zifan Zhao, Huimin Xiang, Heng Chen, Fu-Zhi Dai, Xiaohui Wang, Zhijian Peng, Yanchun Zhou   +6 more
doaj   +1 more source