Low‐temperature sintering of single‐phase, high‐entropy carbide ceramics

2019 ◽  
Vol 102 (12) ◽  
pp. 7217-7224 ◽  
Author(s):  
Lun Feng ◽  
William G. Fahrenholtz ◽  
Gregory E. Hilmas
Keyword(s):  
Low Temperature ◽  
Single Phase ◽  
Low Temperature Sintering ◽  
High Entropy ◽  
Carbide Ceramics

Strength of single‐phase high‐entropy carbide ceramics up to 2300°C

2020 ◽  
Vol 104 (1) ◽  
pp. 419-427 ◽  
Author(s):  
Lun Feng ◽  
Wei‐Ting Chen ◽  
William G. Fahrenholtz ◽  
Gregory E. Hilmas
Keyword(s):  
Single Phase ◽  
High Entropy ◽  
Carbide Ceramics

scholarly journals Local Lattice Distortion in High-Entropy Carbide Ceramics

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1399
Author(s):  
Huijuan Ge ◽  
Chengfeng Cui ◽  
Hongquan Song ◽  
Fuyang Tian
Keyword(s):  
Single Phase ◽  
Lattice Distortion ◽  
Formation Enthalpy ◽  
Atomic Radius ◽  
Alloying Element ◽  
Local Lattice Distortion ◽  
High Entropy ◽  
Local Lattice ◽  
Binary Carbide ◽  
Carbide Ceramics

Using the ab initio calculations, we study the lattice distortion of HfNbTaTiVC5, HfNbTaTiZrC5 and MoNbTaTiVC5 high-entropy carbide (HEC) ceramics. Results indicate that the Bader atomic radius and charge transfer in HECs is very close to those from binary carbide. The degree of lattice distortion strongly depends on the alloying element. The Bader atomic radius can excellently describe the lattice distortion in HEC. Further, the corresponding atomic radius and formation enthalpy of binary carbides may be indicators to predict the single-phase HECs.

scholarly journals Design high-entropy carbide ceramics from machine learning

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Jun Zhang ◽  
Biao Xu ◽  
Yaoxu Xiong ◽  
Shihua Ma ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Rational Design ◽  
Single Phase ◽  
Transition Metal Carbide ◽  
High Entropy ◽  
Application Potential ◽  
High Prediction ◽  
Chemical Descriptors ◽  
Carbide Ceramics

AbstractHigh-entropy ceramics (HECs) have shown great application potential under demanding conditions, such as high stresses and temperatures. However, the immense phase space poses great challenges for the rational design of new high-performance HECs. In this work, we develop machine-learning (ML) models to discover high-entropy ceramic carbides (HECCs). Built upon attributes of HECCs and their constituent precursors, our ML models demonstrate a high prediction accuracy (0.982). Using the well-trained ML models, we evaluate the single-phase probability of 90 HECCs that are not experimentally reported so far. Several of these predictions are validated by our experiments. We further establish the phase diagrams for non-equiatomic HECCs spanning the whole composition space by which the single-phase regime can be easily identified. Our ML models can predict both equiatomic and non-equiatomic HECs based solely on the chemical descriptors of constituent transition-metal-carbide precursors, which paves the way for the high-throughput design of HECCs with superior properties.

Low Temperature Sintering and Tunable Dielectrics Properties of Thick Films added of Li2CO3on BST

2006 ◽  
Vol 16 (12) ◽  
pp. 747-753
Author(s):  
So-Hyun Jeon ◽  
In-Sung Kim ◽  
Sun-Jong Jung ◽  
Jae-Sung Song ◽  
Jon-Do Yoon
Keyword(s):  
Low Temperature ◽  
Thick Films ◽  
Low Temperature Sintering ◽  
Tunable Dielectrics

Micro X-Ray Diffraction Study of Polycrystalline MgB 2 Wire and Evaluation of Carbon Doping Effect Via Low-Temperature Sintering

2019 ◽  
Author(s):  
Minoru Maeda ◽  
Dipak Patel, Dr. ◽  
Hiroaki Kumakura, Dr. ◽  
Gen Nishijima, Dr. ◽  
Akiyoshi Matsumoto, Dr. ◽  
...  
Keyword(s):  
Low Temperature ◽  
Diffraction Study ◽  
Carbon Doping ◽  
Doping Effect ◽  
Low Temperature Sintering ◽  
X Ray Diffraction ◽  
X Ray
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