应用晶格化的动力学Monte Carlo方法，研究了钛酞菁配合物(TiPc)在不同反应温度和氢氧气体分压比下的催化活性，并与铂(Pt)催化剂进行了比较.一方面，得到了如下预测结果：在470～570K温度区间内TiPc催化剂活性随温度呈非线性关系增加；TiPc催化剂的活性区域在氢氧分压比值 0.2 至2之间；在相同条件下TiPc催化剂活性高于普通Pt催化剂.另一方面，从计算化学工具的角度，验证了动力学Monte Carlo方法用于研究未知催化剂催化活性的可行性.
The lattice kinetic Monte Carlo method is applied to investigate the catalytic activity of the titanium phthalocyanine complex (TiPc) at different temperatures and partial pressure ratios of H2 and O2, and TiPc is compared with platinum (Pt) catalyst. On one hand, the study results show that: the activity of TiPc increases with the temperature between 470-570K; the active range of TiPc is between 0.2 to 2 of H2/O2 partial pressure ratio; under same conditions, the activity of TiPc is higher than that of Pt. On the other hand, from the perspective of computational chemistry tools, the feasibility of kinetic Monte Carlo method is verified for the study of unknown potential catalysts’ catalytic activity.
［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.
