Yao Yonggang, Liu Zhenyu, Xie Pengfei, Huang Zhennan, Li Tangyuan, Morris David, Finfrock Zou, Zhou Jihan, Jiao Miaolun, Gao Jinlong, Mao Yimin, Miao Jianwei John, Zhang Peng, Shahbazian-Yassar Reza, Wang Chao, Wang Guofeng, Hu Liangbing
Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742, USA.
Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Sci Adv. 2020 Mar 13;6(11):eaaz0510. doi: 10.1126/sciadv.aaz0510. eCollection 2020 Mar.
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 NH 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.
多元素合金纳米颗粒(MEA-NPs)在几乎无限的成分空间中发现催化剂方面具有巨大潜力。然而,合理且可控地合成这些本质上复杂的结构仍然是一项挑战。在此,我们报告了通过计算辅助、熵驱动设计和合成高效且耐用的催化剂MEA-NPs。该计算策略包括对数百万种成分进行预筛选、通过密度泛函理论计算预测合金形成以及通过混合蒙特卡罗和分子动力学方法检查结构稳定性。选定的成分能够在高温(例如1500 K,0.5秒)下高效快速地合成,且具有出色的热稳定性。我们将这些MEA-NPs应用于催化氨分解,由于多元素混合、小尺寸和合金相的协同效应,观察到了出色的性能。我们预计,计算辅助的MEA-NPs合理设计和快速合成广泛适用于各种催化反应,并将加速材料发现。