Thermodynamics and Catalytic Activity of Ruthenium Oxides Grown on Ruthenium Metal from a Machine Learning Atomic Simulation

Author(s):  
Ze-Yi Zhu ◽  
Ye-Fei Li ◽  
Cheng Shang ◽  
Zhi-Pan Liu
2018 ◽  
Vol 20 (47) ◽  
pp. 30006-30020 ◽  
Author(s):  
Wenwen Li ◽  
Yasunobu Ando

Recently, the machine learning (ML) force field has emerged as a powerful atomic simulation approach because of its high accuracy and low computational cost.


2020 ◽  
Vol 263 ◽  
pp. 118257 ◽  
Author(s):  
Alexander Smith ◽  
Andrea Keane ◽  
James A. Dumesic ◽  
George W. Huber ◽  
Victor M. Zavala

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Francis D. Mayer ◽  
Pooya Hosseini-Benhangi ◽  
Carlos M. Sánchez-Sánchez ◽  
Edouard Asselin ◽  
Előd L. Gyenge

Abstract The electroreduction of CO2 is one of the most investigated reactions and involves testing a large number and variety of catalysts. The majority of experimental electrocatalysis studies use conventional one-sample-at-a-time methods without providing spatially resolved catalytic activity information. Herein, we present the application of scanning electrochemical microscopy (SECM) for simultaneous screening of different catalysts forming an array. We demonstrate the potential of this method for electrocatalytic assessment of an array consisting of three Sn/SnOx catalysts for CO2 reduction to formate (CO2RF). Simultaneous SECM scans with fast scan (1 V s−1) cyclic voltammetry detection of products (HCOO−, CO and H2) at the Pt ultramicroelectrode tip were performed. We were able to consistently distinguish the electrocatalytic activities of the three compositionally and morphologically different Sn/SnOx catalysts. Further development of this technique for larger catalyst arrays and matrices coupled with machine learning based algorithms could greatly accelerate the CO2 electroreduction catalyst discovery.


2020 ◽  
Vol 8 (34) ◽  
pp. 17507-17515
Author(s):  
Ze Yang ◽  
Wang Gao ◽  
Qing Jiang

We develop a universal design scheme based on the machine learning method and the intrinsic properties of substrates and adsorbates, allowing accurate prediction and rapid screening through a large phase space of alloys and multiple adsorbates.


2019 ◽  
Vol 162 ◽  
pp. 126-135 ◽  
Author(s):  
Hongxiang Zong ◽  
Yufei Luo ◽  
Xiangdong Ding ◽  
Turab Lookman ◽  
Graeme J. Ackland

2019 ◽  
Vol 5 (11) ◽  
pp. 10-17
Author(s):  
A. Stepacheva ◽  
A. Semenova ◽  
N. Yablokova ◽  
E. Kupriyanova ◽  
D. Rud

In this paper, the possibility of using a magnetically separated ruthenium-containing catalyst based on a polymer matrix of hypercrosslinked polystyrene in the supercritical deoxygenation of stearic acid to produce a second-generation biodiesel fuel is studied. The catalyst was synthesized by a successive deposition of iron and ruthenium oxides to the polymeric support. The resulting catalytically active Ru-Fe3O4-HPS system is characterized by high specific surface area (364 m2/g) and magnetization (4.5 emu/g). This catalyst allows obtaining a high (up to 86%) yield of hydrocarbons C17+ and exhibits high activity in the process of deoxygenation in supercritical n-hexane. It was found that the selected catalytic system retains its catalytic activity for at least 10 consecutive cycles.


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