向下鼓气泡进入水或磁性液体的数值模拟与实验验证

应用数值与实验的方法分析了气泡向下鼓入水和磁性液体的过程,通过数学模型应用PLIC-VOF方法仿真重现了气泡的形成与破裂机理,通过数值模拟获得了气泡的形状与生长周期,用CCD摄像头完成实验测量,实验与模拟达到了数值上的吻合

向下鼓气泡进入水或磁性液体的数值模拟与实验验证

应用数值与实验的方法分析了气泡向下鼓入水和磁性液体的过程,通过数学模型应用PLIC-VOF方法仿真重现了气泡的形成与破裂机理,通过数值模拟获得了气泡的形状与生长周期,用CCD摄像头完成实验测量,实验与模拟达到了数值上的吻合

numericalsimulationandexperimentalverificationofgasbubbleemittingdownwardsintowaterormagneticfluid

应用数值与实验的方法分析了气泡向下鼓入水和磁性液体的过程,通过数学模型应用PLIC-VOF方法仿真重现了气泡的形成与破裂机理,通过数值模拟获得了气泡的形状与生长周期,用CCD摄像头完成实验测量,实验与模拟达到了数值上的吻合

Covalency competition dominates the water oxidation structure-activity relationship on spinel oxides

Spinel oxides have attracted growing interest over the years for catalysing the oxygen evolution reaction (OER) due to their efficiency and cost-effectiveness, but fundamental understanding of their structure–property relationships remains elusive. Here we demonstrate that the OER activity on spinel oxides is intrinsically dominated by the covalency competition between tetrahedral and octahedral sites. The competition fabricates an asymmetric MT−O−MO backbone where the bond with weaker metal–oxygen covalency determines the exposure of cation sites and therefore the activity. Driven by this finding, a dataset with more than 300 spinel oxides is computed and used to train a machine-learning model for screening the covalency competition in spinel oxides, with a mean absolute error of 0.05 eV. [Mn]T[Al0.5Mn1.5]OO4 is predicted to be a highly active OER catalyst and subsequent experimental results confirm its superior activity. This work sets mechanistic principles of spinel oxides for water oxidation, which may be extendable to other applications.Ministry of Education (MOE)National Research Foundation (NRF)Accepted versionThis work was supported by Singapore Ministry of Education Tier 2 Grant (MOE2018-T2-2-027) and the Singapore National Research Foundation under its Campus for Research Excellence And Technological Enterprise (CREATE) programme. We thank the Facility for Analysis, Characterization, Testing, and Simulation (FACTS) in Nanyang Technological University. This research used resources of the National Synchrotron Light Source II, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under contract no. DE-SC0012704. We also appreciate the XAS measurements from SSLS, soft X-ray and ultraviolet beamline. Y.S. and Z.X. thank A. Lapkin (University of Cambridge) for helpful discussion on machine-learning concepts and thank L. Zeng (Southern University of Science and Technology) for helpful discussion on catalyst performance. H.Z. gives thanks for the support from ITC via the Hong Kong Branch of National Precious Metals Material (NPMM) Engineering Research Center, and the start-up grant (project no. 9380100) and grants (project no. 9610478 and 1886921) in City University of Hong Kon