Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Autor: Jinlong Gao, Jihan Zhou, Zhenyu Liu, Yonggang Yao, Miaolun Jiao, Peng Zhang, Zhennan Huang, Reza Shahbazian-Yassar, Liangbing Hu, David Morris, Tangyuan Li, Zou Finfrock, Chao Wang, Guofeng Wang, Jianwei John Miao, Yimin Mao, Pengfei Xie
Rok vydání: 2020
Předmět:
Zdroj: Science Advances
ISSN: 2375-2548
Popis: We developed a computationally aided alloy synthesis in the multi-element space toward highly efficient and durable catalysts.
Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH3 decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.
Databáze: OpenAIRE