文献类型：期刊文献

英文题名：High-Throughput Screening of Alloy Catalysts for Dry Methane Reforming

作者：Yu, Ya-Xin[1];Yang, Jie[1];Zhu, Ka-Ke[1];Sui, Zhi-Jun[1];Chen, De[2];Zhu, Yi-An[1];Zhou, Xing-Gui[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, UNILAB, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[2]Norwegian Univ Sci & Technol, Dept Chem Engn, N-9491 Trondheim, Norway

年份：2021

卷号：11

期号：14

起止页码：8881

外文期刊名：ACS CATALYSIS

收录：;EI(收录号：20213010678752);WOS:【SCI-EXPANDED(收录号:WOS:000674927200045)】；

基金：This work was supported by the National Natural Science Foundation of China (U1663221, 22073027, and 91645122), the Natural Science Foundation of Shanghai (20ZR1415800), and the Fundamental Research Funds for the Central Universities (222201718003). The computational time provided by the Notur project is highly acknowledged.

语种：英文

外文关键词：density functional theory; rational catalyst design; microkinetic analysis; adsorbate-adsorbate interactions; dry methane reforming; machine learning

摘要：Dry methane reforming (DMR) is a promising technique aiming at converting two major greenhouse gases into useful chemical feedstocks. A major challenge in the commercialization of this process is to develop a suitable catalyst with long-term stability, strong catalytic activity, and low cost. In this work, a microkinetic analysis coupled with a descriptor-based approach is conducted to study the trend in the catalytic activity across eight transition metals, where the formation energies of adsorbed C and O are identified as two reactivity descriptors. The catalytic properties of the close-packed (111) and stepped (211) surfaces are compared to show the structure sensitivity of the DMR reaction. The resultant activity map with adsorbate-adsorbate interactions taken into consideration shows that Rh, Ir, and Ni are among the most active elemental metals for this reaction. Then, 1482 A(3)B(1) and 741 A(1)B(1) alloys that contain 39 elements have been screened for the DMR catalyst based on their anticarbonization and antioxidation ability, catalytic activity, and cost, in which an unsupervised machine learning technique is employed to identify the thermodynamically stable alloys upon adsorption and thus to accelerate the screening process. The identification of 23 binary intermetallic compounds as potential DMR catalysts not only gives theoretical evidence in support of the experimentally reported combinations but also provides new guidelines for rationally designing alloy catalysts for the DMR reaction.