Journal of Shanghai Jiaotong University ›› 2019, Vol. 53 ›› Issue (4): 497-503.doi: 10.16183/j.cnki.jsjtu.2019.04.015
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ZHOU Sua,b,HU Zhea,GAO Yangb,JIANG Zhenb
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2019-04-28
Published:
2019-04-28
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ZHOU Su,HU Zhe,GAO Yang,JIANG Zhen. Catalytic Capacity of Titanium Phthalocyanine for Redox Reactions Complex Based on Lattice Kinetic Monte Carlo Method[J]. Journal of Shanghai Jiaotong University, 2019, 53(4): 497-503.
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URL: https://xuebao.sjtu.edu.cn/EN/10.16183/j.cnki.jsjtu.2019.04.015
[1]高阳. 利用计算化学方法对铑催化的脱羰基反应和酸促进的反电子需求Diels-Alder 反应机理的研究[D]. 武汉: 华中师范大学, 2015. GAO Yang. Computational exploration of the me-chanism of organic reactions: Rh(I)-catalyzed decar-bonylation reaction and acid-promoted inverse-electron demand Diels-Alder reaction[D]. Wuhan: Central China Normal University, 2015. [2]周苏, 姜缜, DE LILE J R. 基于密度泛函理论的过渡金属酞菁配合物氧还原反应催化能力[J]. 上海交通大学学报, 2017, 51(12): 1422-1427. ZHOU Su, JIANG Zhen, DE LILE J R. Catalytic capacity of transition metal phthalocyanine complexes based on density functional theory[J]. Journal of Shanghai Jiao Tong University, 2017, 51(12): 1422-1427. [3]SENFTLE T P, VAN DUIN A C T, JANIK M J. Determining in situ phases of a nanoparticle catalyst via grand canonical Monte Carlo simulations with the ReaxFF potential[J]. Catalysis Communications, 2014, 52: 72-77. [4]汤星舟. 结合动态蒙特卡洛法与分子动力学研究c面蓝宝石衬底的氮化现象[D]. 厦门: 厦门大学, 2014. TANG Xingzhou. Kinetic process of nitridation on the α-sapphire surface in the combination of KMC and MD[D]. Xiamen: Xiamen University, 2014. [5]HESS F, OVER H. Kinetic Monte Carlo simulations of heterogeneously catalyzed oxidation Reactions[J]. Catalysis Science & Technology, 2014, 4(3): 583-598. [6]LI J D, CROISET E, RICARDES-SANDOVAL L. Carbon nanotube growth: First-principles-based kinetic Monte Carlo model[J]. Journal of Catalysis, 2015, 326: 15-25. [7]GUO X, MINAKATA D, CRITTENDEN J. Computer-based first-principles kinetic Monte Carlo simulation of polyethylene glycol degradation in aqueous phase UV/H2O2 advanced oxidation process[J]. Environmental Science & Technology, 2014, 48(18): 10813-10820. [8]CHORKENDORFF I, NIEMANTSVERDRIET J W. Concepts of modern catalysis and kinetics[M]. Weinheim: Wiley-VCH, 2003. [9]SCHAEFER C, JANSEN A P J. Coupling of kinetic Monte Carlo simulations of surface reactions to transport in a fluid for heterogeneous catalytic reactor modeling[J]. Journal of Chemical Physics, 2013, 138(5): 054102. [10]HESS F. DFT-based kinetic Monte Carlo simulations of oxidation reactions over the RuO2 (110) model catalyst surface[D]. Giessen: Justus-Liebig-University, 2015. [11]KEIL F J. Complexities in modeling of heterogeneous catalytic reactions[J]. Computers & Mathematics with Applications, 2013, 65(10): 1674-1697. [12]魏子栋. 质子交换膜燃料电池催化剂性能增强方法研究进展[J]. 化工进展, 2016, 35(9): 2629-2639. WEI Zidong. Advances of the catalytic performance enhancement for proton exchange membrane fuel cells[J]. Chemical Industry and Engineering Progress, 2016, 35(9): 2629-2639. [13]DEUTSCHMANN O, KNZINGER H, KOCHLOEFL K, et al. Heterogeneous catalysis and solid catalysts[M]. Weinheim: Wiley-VCH, 2009. [14]CHOI S, SANG B, HONG J I. Catalytic behavior of metal catalysts in high-temperature RWGS reaction: In-situ FT-IR experiments and first-principles calculations[J]. Scientific Reports, 2017(7): 41207. [15]陈维民, 孙公权, 毛庆, 等. 直接甲醇燃料电池催化剂性能的影响因素[J]. 催化学报, 2007, 28(8): 703-708. CHEN Weimin, SUN Gongquan, MAO Qing, et al. Factors influencing the performance of catalysts in direct methanol fuel cells[J]. Chinese Journal of Cata-lysis, 2007, 28(8): 703-708. |
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