文献类型：期刊文献

英文题名：Atomic Design of Alkyne Semihydrogenation Catalysts via Active Learning

作者：Ge, Xiaohu[1];Yin, Jun[3,4];Ren, Zhouhong[2];Yan, Kelin[1];Jing, Yundao[1];Cao, Yueqiang[1];Fei, Nina[1];Liu, Xi[2];Wang, Xiaonan[3,4];Zhou, Xinggui[1];Chen, Liwei[2];Yuan, Weikang[1];Duan, Xuezhi[1]

机构：[1]East China Univ Sci & Technol, State Key Lab Chem Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Frontiers Sci Ctr Transformat Mol, Insitu Ctr Phys Sci, Sch Chem & Chem Engn, Shanghai 200240, Peoples R China;[3]Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China;[4]Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore

年份：2024

卷号：146

期号：7

起止页码：4993

外文期刊名：JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

收录：;EI(收录号：20240815582560);WOS:【SCI-EXPANDED(收录号:WOS:001167221500001)】；

基金：This work was financially supported by the National Key R&D Program of China (2022YFA1503502 and 2022YFA1503503), the Natural Science Foundation of China (22008067, 22072090 and 22178100), the Chang Jiang Scholars Program of the Ministry of Education of China (T2022163), Tsinghua University Initiative Scientific Research Program, Shanghai Rising-star Program (23QA1401900), the Innovation Program of Shanghai Municipal Education Commission, the Shanghai Science and Technology Innovation Action Plan (22JC1403800), the Program of Shanghai Academic/Technology Research Leader (21XD1421000), Young Elite Scientists Sponsorship Program by CAST (2023QNRC001), and the Fundamental Research Funds for the Central Universities. We thank the BL11B and BL14W1 XAFS beamlines of Shanghai Synchrotron Radiation Facility (SSRF) for providing the beamtime.

语种：英文

外文关键词：Artificial intelligence - Atoms - Binary alloys - Catalyst selectivity - Dehydrogenation - Density functional theory - Ethylene

摘要：Alkyne hydrogenation on palladium-based catalysts modified with silver is currently used in industry to eliminate trace amounts of alkynes in alkenes produced from steam cracking and alkane dehydrogenation processes. Intensive efforts have been devoted to designing an alternative catalyst for improvement, especially in terms of selectivity and catalyst cost, which is still far away from that as expected. Here, we describe an atomic design of a high-performance Ni-based intermetallic catalyst aided by active machine learning combined with density functional theory calculations. The engineered NiIn catalyst exhibits >97% selectivity to ethylene and propylene at the full conversion of acetylene and propyne at mild temperature, outperforming the reported Ni-based catalysts and even noble Pd-based ones. Detailed mechanistic studies using theoretical calculations and advanced characterizations elucidate that the atomic-level defined coordination environment of Ni sites and well-designed hybridization of Ni 3d with In 5p orbital determine the semihydrogenation pathway.