Well-defined high entropy-metal nanoparticles:Detection of the multi-element particles by deep learning  

作　　者：Manar Alnaasan Wail Al Zoubi Salh Alhammadi Jee-Hyun Kang Sungho Kim Young Gun Ko 

机构地区：[1]Department of Electronics Engineering,Yeungnam Univeristy,280 Daeha-ro,Gyeongsan,38541,Republic of Korea [2]Materials Electrochemistry Laboratory,School of Materials Science and Engineering,Yeungnam University,Gyeongsan 38541,Republic of Korea [3]Department of Future Energy Convergence,Seoul National University of Science and Technology,232 Gongneung-Ro,Nowon-Gu,Seoul 01811,Republic of Korea [4]School of Materials Science and Engineering,Institute of Materials Technology,Yeungnam University,Gyeongsan 38541,Republic of Korea

出　　处：《Journal of Energy Chemistry》2024年第11期262-273,共12页能源化学（英文版）

基　　金：National Research Foundation (NRF) of South Korea (NRF-2022R1A2C1004392);Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (IRIS RS-202300240109)。

摘　　要：Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the heterogeneity and large size at the nanoscale,the poorly defined catalyst nanostructure,and thermodynamic immiscibility of the strongly repelling metallic elements.To address these challenges,an ultrasonic-assisted coincident electro-oxidation-reduction-precipitation(U-SEO-P) is presented to fabricate ultra-stable PtRuAgCoCuP NPs,which produces numerous active intermediates and induces strong metal-support interactions.To sort the active high-entropy mNPs,individual NPs are described on the support surface and the role of deep learning in understanding/predicting the features of PtRuAgCoCu@TiO_(x) catalysts is explained.Notably,this deep learning approach required minimal to no human input.The as-prepared PtRuAgCoCu@TiO_(x) catalysts can be used to catalyze various important chemical reactions,such as a high reduction conversion(100% in 30 s),with no loss of catalytic activity even after 20 cycles of nitroarene and ketone/aldehyde,which is several times higher than commercial Pt@TiO_(x) owing to individual PtRuAgCoCuP NPs on TiO_(x) surface.In this study,we present the "Totally Defined Catalysis" concept,which has enormous potential for the advancement of high-activity catalysts in the reduction of organic compounds.

关 键 词：Metal nanoparticles Deep learning CATALYST REDUCTION 

分 类 号：TQ426[化学工程] TP18[自动化与计算机技术—控制理论与控制工程] TB383.1[自动化与计算机技术—控制科学与工程]